Noninvasive blood pressure pulse detection and blood pressure - Jultika

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OULU 2006

UNIVERSITY OF OULU P.O. Box 7500 FI-90014 UNIVERSITY OF OULU FINLAND

U N I V E R S I TAT I S

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SCIENTIAE RERUM NATURALIUM Professor Mikko Siponen HUMANIORA Professor Harri Mantila

TECHNICA Professor Juha Kostamovaara

ACTA

U N I V E R S I T AT I S O U L U E N S I S

Hannu Sorvoja

E D I T O R S

Hannu Sorvoja

A B C D E F G

O U L U E N S I S

ACTA

A C TA

C 259

NONINVASIVE BLOOD PRESSURE PULSE DETECTION AND BLOOD PRESSURE DETERMINATION

MEDICA Professor Olli Vuolteenaho

SCIENTIAE RERUM SOCIALIUM Senior Assistant Timo Latomaa SCRIPTA ACADEMICA Communications Officer Elna Stjerna OECONOMICA Senior Lecturer Seppo Eriksson

EDITOR IN CHIEF Professor Olli Vuolteenaho

EDITORIAL SECRETARY Publications Editor Kirsti Nurkkala ISBN 951-42-8271-X (Paperback) ISBN 951-42-8272-8 (PDF) ISSN 0355-3213 (Print) ISSN 1796-2226 (Online)

FACULTY OF TECHNOLOGY, DEPARTMENT OF ELECTRICAL AND INFORMATION ENGINEERING, INFOTECH OULU, UNIVERSITY OF OULU

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TECHNICA

ACTA UNIVERSITATIS OULUENSIS

C Te c h n i c a 2 5 9

HANNU SORVOJA

NONINVASIVE BLOOD PRESSURE PULSE DETECTION AND BLOOD PRESSURE DETERMINATION

Academic dissertation to be presented, with the assent of the Faculty of Technology of the University of Oulu, for public defence in Raahensali (Auditorium L10), Linnanmaa, on December 8th, 2006, at 12 noon

O U L U N Y L I O P I S TO, O U L U 2 0 0 6

Copyright © 2006 Acta Univ. Oul. C 259, 2006

Supervised by Professor Risto Myllylä

Reviewed by Professor Pasi Karjalainen Professor Tadeusz Pustelny

ISBN 951-42-8271-X (Paperback) ISBN 951-42-8272-8 (PDF) http://herkules.oulu.fi/isbn9514282728/ ISSN 0355-3213 (Printed) ISSN 1796-2226 (Online) http://herkules.oulu.fi/issn03553213/

Cover design Raimo Ahonen

OULU UNIVERSITY PRESS OULU 2006

Sorvoja, Hannu, Noninvasive blood pressure pulse detection and blood pressure determination Faculty of Technology, University of Oulu, P.O.Box 4000, FI-90014 University of Oulu, Finland, Department of Electrical and Information Engineering, Infotech Oulu, University of Oulu, P.O.Box 4500, FI-90014 University of Oulu, Finland Acta Univ. Oul. C 259, 2006 Oulu, Finland

Abstract This thesis describes the development of pressure sensor arrays and a range of methods suitable for the long-term measurement of heart rate and blood pressure determination using a cuff and a pressure sensor array on the radial artery. This study also reviews the historical background of noninvasive blood pressure measurement methods, summarizes the accuracies achieved and explains the requirements for common national and international standards of accuracy. Two prototype series of pressure transducer arrays based on electro-mechanical film (EMFi) were designed and tested. By offering high (∼TΩ) resistance, EMFi is an excellent material for low-current long-term measurement applications. About 50 transducer arrays were built using different configurations and electrode materials to sense low-frequency pressure pulsations on the radial artery in the wrist. In addition to uniform quality, essential requirements included an adequate linear response in the desired temperature range. Transducer sensitivity was tested as a function of temperature in the range of 25–45 °C at varying static and alternating pressures. The average sensitivity of the EMFi used in the transducers proved adequate (∼2.2 mV/mmHg and ∼7 mV/mmHg for normal and high sensitive films) for the intended purpose. The thesis also evaluates blood pressure measurements by the electronic palpation method (EP) and compares the achieved accuracy to that of the oscillometric method (OSC) using average intraarterial (IA) blood pressure as a reference. All of these three measurements were made simultaneously for each person. In one test group, measurements were conducted on healthy volunteers in sitting and supine position during increasing and decreasing cuff pressure. Another group, comprising elderly cardiac patients, was measured only in the supine position during cuff inflation. The results showed that the EP method was approximately as accurate as the OSC method with the healthy subjects and slightly more accurate with the cardiac patient group. The advantage of the EP method is that also the wave shape and velocity of arterial pressure pulses is available for further analysis, including the assessment of arterial stiffness.

Keywords: blood pressure monitoring, cuff, pressure transducer, pulse transit time, pulse wave velocity, radial artery pulsation

Acknowledgements Focusing on cross-scientific research on blood pressure measurement methods, the work leading to this thesis was carried out mainly during 1993-2002 in the Department of Electrical and Information Engineering at the University of Oulu. First of all, I would like to thank Prof. Risto Myllylä for encouraging me in various ways to finalize the thesis and also for arranging time to do it. I also wish to express my deep gratitude to my parents, Kaarlo (deceased) and Linnea Sorvoja, and my sisters and brothers, who have offered unstinting support during my whole life. My father’s early death in 1992 due to a cardiovascular condition provided the motivation to focus on biomedical engineering research, when it was time to start my Diploma Thesis. I owe my warmest thanks to my wife Irene, her children and grandchildren. They have brought meaning and joy into my life. I am grateful to Polar Electro Ltd. for funding the projects and to the Tauno Tönning Foundation for supporting this work through two grants. I also wish to express my thanks to personnel of the Oulu University Hospital and Northern Ostrobothnia Hospital District, especially Päivi Kärjä-Koskenkari, Juha Koskenkari, Mauno Lilja and Y. Antero Kesäniemi, for providing me the opportunity to conduct the necessary measurements at the hospital. Moreover, I wish to thank Seppo Nissilä, Vuokko-Marjut Kokko, Jukka Hast, Antti Ruha, Eija Vieri-Gashi, Jari Miettinen, and Mika Sorvisto, previous co-workers, for participation in the research. I would also like to convey my appreciation to the entire personnel in the Optoelectronics and Measurement Techniques Laboratory for creating a pleasant working environment. My fellow-workers in the Electronics Laboratory and the whole staff at the department’s workshop deserve the warmest thanks for making the mechanical components for the measurement system. Last, but not least, I also wish to extend my sincerest thanks to my friends, who have remained close to me since my student days. Oulu, October 2006

Hannu Sorvoja

List of terms, symbols and abbreviations Ω £ °C A C Hz m mmHg s V W

Ohm Pound degrees Celsius Ampere Coulomb Hertz meter millimeter of mercury, unit for pressure, 133 Pascals second Volt Watt

α ρ τ µ c CR

constant depending on arterial properties symbol for blood density symbol for time constant micro, 10-6 symbol for the velocity of blood pressure pulses symbol for characteristic ratio of the diastolic or systolic determination ratio of the maximum pulse amplitude in the oscillometric blood pressure measurement method symbol for thickness of an artery symbol for derivative of pressure mathematical constant (Euler), base of the natural logarithm, 2.71828… symbol for modulus of elasticity mill, 10-3 pico, 10-12 symbol for pressure symbol for correlation coefficient tera, 1012, temperature, (also Tesla) symbol for output voltage

d dp/dt e E m p p, P R T Uout

A/A

accuracy classification grade in the AAMI and BHS standards for a device measuring systolic and diastolic blood pressures AAMI American Association for the Advancement of Medical Instrumentation BHS British Hypertension Society BP blood pressure, band-pass (filtering) CMOS complementary metal-oxide-semiconductor DC direct current, zero frequency DIAS diastolic blood pressure ECG electrocardiogram EMFi trademark for electromechanical film of EMFit, Ltd. EP electronic palpation blood pressure measurement method ESD electro static discharge ESH European Society of Hypertension HF high frequency HW hardware IA intra-arterial IABP intra-arterial blood pressure ISH International Society of Hypertension Kapton trademark of Dupont, Ltd., for a polyimide material used for PCB and films LF low frequency LP low-pass (filtering) MAP mean arterial pressure OOK on-off-keying OSC oscillometric blood pressure measurement method PAT pulse arrival time PC personal computer PCB printed circuit board PPG photoplethysmography PTT pulse transit time PWV pulse wave velocity RAM random access memory ROM read only memory SW software SYS systolic blood pressure ™ trade mark VLF very low frequency WHO World Health Organization

List of original papers I

Ruha, A, Miettinen J, Sorvoja H & Nissilä S (1996) Heart rate measurement based on detection of the radial artery pressure pulse. Proc. BIOSIGNAL’96, Brno, Czech Republic, 1: 198–200. II Sorvoja H, Kokko V-M, Myllylä R & Miettinen J (2005) Use of EMFi as a blood pressure pulse transducer, IEEE Transactions on Instrumentation and Measurement 54(6): 2505–2512. III Nissilä S, Sorvisto M, Sorvoja H, Vieri-Gashi E & Myllylä R (1998) Noninvasive blood pressure measurement based on the electronic palpation method. Proc. 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, USA, 20(4): 1723–1726. IV Sorvoja H, Myllylä R, Nissilä S, Kärjä-Koskenkari P, Koskenkari J, Lilja M & Kesäniemi YA (2001) A method to determine diastolic blood pressure based on pressure pulse propagation in the electronic palpation method, Proc. 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, 4: 3255–3258. V Sorvoja H, Myllylä R, Nissilä S, Kärjä-Koskenkari P, Koskenkari J, Lilja M & Kesäniemi YA (2001) Systolic blood pressure accuracy enhancement in the electronic palpation method using pulse waveform, Proc. 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, 1: 222–225. VI Sorvoja H, Hast J, Myllylä R, Kärjä-Koskenkari P, Nissilä S & Sorvisto M (2003) Blood pressure measurement method using pulse-transit-time. Molecular and Quantum Acoustics 24: 169–181. VII Sorvoja H & Myllylä R (2004) Accuracy of the electronic palpation blood pressure measurement method versus the intra-arterial method. Technology and Health Care, International Journal of Health Care Engineering 12(2): 145–146. VIII Sorvoja H, Myllylä R, Kärjä-Koskenkari P, Koskenkari J, Lilja M & Kesäniemi YA (2005) Accuracy comparison of oscillometric and electronic palpation blood pressure measuring methods using intra-arterial method as a reference. Molecular and Quantum Acoustics 26: 235–260. IX Hast J, Myllylä R, Sorvoja H & Miettinen J (2002) Arterial pulse wave shape measurement using self-mixing effect in a diode laser, Quantum Electronics 32(11): 975–980.

In the following presentation, the papers above will be referred to by the Roman numerals I – IX. Paper I deals with research which employed an eight-channel EMFi sensor to measure heart rate in non-moving and moderately moving subjects. Both the sensor and the amplifiers were designed and built by the author, who was also responsible for the test measurements. Paper II concentrated on the EMFi sensor material, especially on the temperature dependence of its sensitivity. Other main concerns were static and dynamic pressure and statistical sensitivity deviations between the different channels and different sensors. This work was done and presented earlier in the author’s Licentiate Thesis (1998) and in the Master’s Thesis (1999) of Vuokko-Marjut Kokko, who conducted her research under the current author’s supervision. The sensor was further developed and patented by Polar Electro Ltd. (1998), which also funded the research projects focusing on blood pressure and pulse measurements during 1993-2000. The same sensor type was also used in blood pressure measurements. In 1994, the author demonstrated with self-made EMFI sensors and measurement electronics, which included a personal computer with data acquisition boards and analysis software that when cuff pressure on the upper arm drops below the systolic level, the amplitude of pressure pulses detected in the radial artery increases. Continuing to increase until the diastolic blood pressure level is reached, the amplitude then stabilizes to a certain level. The author also developed algorithms that enable determining systolic and diastolic blood pressure from the amplitude curve. Based on line fittings, these algorithms work both for decreasing and increasing cuff pressure, as demonstrated by measurements on healthy volunteers. After that, further development was done by Mika Sorvisto in his Master’s thesis of 1996. Sorvisto conducted measurements at the Oulu University Hospital on 23 persons, mostly young, healthy volunteers. The author contributed to the research by developing an eight-channel sensor with amplifiers and band-pass filtering electronics. The results were published in Paper III. This blood pressure measurement method was then patented by Polar Electro Ltd. (1999). It was established in these measurements that there was a difference in the accuracy of diastolic blood pressure determination between healthy persons and hospital patients. Therefore, during the winter of 1998/1999, the current author used new measurement devices to run a second set of measurements on patients who had undergone a cardiac operation. The method reported in Paper III was based on detecting pulse amplitude changes, which later proved an unreliable method, leading to the development of another method, based on pulse transit time changes, as documented in Paper IV. All the algorithms and analyses in Papers IV-VIII were made by the author. These papers focused on the accuracy of the developed blood pressure measurement method, which was compared to measurements performed simultaneously with the intra-arterial and the oscillometric method. The last paper (IX) describes an alternative to the use of a pressure sensor to detect pulsations in the radial artery, namely, the laser-Doppler method. This is a feasible alternative when the test subject is immobile. In this work, the author concentrates mainly on providing application-specific advice.

Contents Abstract Acknowledgements List of terms, symbols and abbreviations List of original papers Contents 1 Introduction ................................................................................................................... 13 1.1 Motivation to this work ..........................................................................................13 1.2 Contribution of the thesis .......................................................................................14 1.3 Organization of the thesis .......................................................................................15 2 Noninvasive blood pressure measurement methods ...................................................... 16 2.1 Counterpressure with sphygmomanometer ............................................................16 2.2 Auscultatory method...............................................................................................17 2.3 Oscillometric method .............................................................................................19 2.4 Volume clamp, or vascular unloading, method.......................................................22 2.5 Tonometric method.................................................................................................24 2.6 Pressure pulse transit-time method.........................................................................26 2.6.1 Photoplethysmography for the pulse transit time method ...............................28 2.6.2 Other measurement methods and sensors for the pulse transit time method...........................................................................30 2.6.3 Standards and accuracy validation...................................................................31 2.7 Blood pressure values.............................................................................................32 2.7.1 Clinically approved blood pressure measurement devices ..............................33 3 EMFi pressure sensor array ........................................................................................... 34 3.1 EMFi sensor material..............................................................................................34 3.2 Preparation of blood pressure sensor ......................................................................36 3.3 Measurement setups for the blood pressure sensor.................................................38 3.4 Sensor measurements .............................................................................................41 4 Blood pressure measurements ....................................................................................... 48 4.1 Measurement setups ...............................................................................................48 4.2 Study subjects .........................................................................................................49 4.3 Blood pressure determination in the electronic palpation method..........................51

4.4 Accuracy of the electronic palpation method..........................................................58 4.5 Blood pressure determination in the oscillometric method ....................................61 4.6 Accuracy of the oscillometric method ....................................................................65 5 Discussion ..................................................................................................................... 67 6 Conclusion..................................................................................................................... 72 References Original papers

1 Introduction 1.1 Motivation to this work Cardiovascular diseases are one of the leading causes of death. Globally, they underlie the death of one third of the world’s population and among Europeans, such as the Finnish, one half of all deaths are attributed to them. These diseases can be divided into coronary, cerebral or peripheral artery diseases. The main cause of cardiovascular diseases is atherosclerosis, in which arterial compliance decreases dramatically with the result that arterial stiffness increases (van Popele et al. 2000 and 2001), which serve to increase the patient’s death risk (Mattace-Raso et al. 2006). This ties in closely with hypertension and type 2 diabetes. Noninvasive measurements for assessing these diseases would be highly valuable for obvious reasons. Such invasive blood pressure measurement methods as are generally used in hospitals, especially in intensive care units, are known to carry a risk, albeit a small one. Risks of these kinds could be avoided, however, if there was a noninvasive method offering a high degree of accuracy and real time operation in a continuous, beat-to-beat, mode. Further, the method should be insensitive to the patient’s movement (artifacts) and respond rapidly to cardiovascular changes, such as a sudden drop in blood pressure. As it is, invasive methods are currently prevalent. In clinics and home care, noninvasive blood pressure measurement devices have become increasingly common during the last decade as their prices have sunk to an appropriate level for ordinary consumers. Automatic measurement features and easiness of use have also contributed to their growing popularity. Nevertheless, the accuracy of these devices has not yet reached the necessary level, since only some of them are clinically validated and most have a questionable accuracy (O’Brien 2001). Originally, the stimulus that led to the work presented in this thesis came from Professor Seppo Säynäjäkangas, the founder of Polar Electro Ltd., a long-time market leader in ambulatory heart rate monitors. The idea was to design a small-sized, ambulatory, low-power noninvasive measurement device and to develop a measurement method for sensing blood pressure pulsations in the radial artery. These pulsations can be used to determine heart rate and blood pressure which, in addition to ECG

14 (electrocardiography) are undoubtedly the most important and most commonly measured biosignals. An ideal site for the measurement is provided by the radial artery in the wrist, since it is located near the skin surface and the wrist serves as a good attachment for the measuring device. In addition, both patients and healthy consumers are unlikely to object to this place of measurement. Moreover, measuring the shape of the arterial pressure pulse and wave velocity allows additional cardiovascular parameters to be assessed, including arterial stiffness, which is a manifestation of atherosclerosis. During the past decade, as measurement methods and sensor technologies have advanced, pulse wave velocity (PWV) measurement has become broadly used and clinically approved (Asmar et al. (1995) and Asmar (1999)). Low-power radial artery pressure sensors are attractive to use, because, together with other signals such as the ECG, they can be used for PWV measurements in addition to heart rate and blood pressure measurements.

1.2 Contribution of the thesis Only a few sensing methods and materials, powered by a single button cell battery, are applicable to long-term, ambulatory monitoring of blood pressure pulses, although these requirements need to be fulfilled in sporting and home care applications as well as in clinical use and intensive care. Of these, sporting applications in particular call for a wider temperature range than the others, which are usually carried out at room temperature. Ideally suited for measurements at that temperature is the recently developed sensor material known as electromechanical film (EMFi). The main contribution of this thesis is the development of a new method for noninvasive blood pressure pulse monitoring in the wrist, based on using EMFi material to obtain systolic and diastolic blood pressures. To determine the presented method’s accuracy, the intra-arterial, invasive method was used as a reference. Next, these values were compared to those obtained by the oscillometric method, which is the most common commercially available method. All measurements using the three different methods were conducted simultaneously. This work could be divided into seven steps: 1. Reviewing current noninvasive methods of determining blood pressure, comparing their accuracy rates and explaining their historical backgrounds. 2. Developing multi-channel pressure transducer arrays that are accurate enough for blood pressure pulse detection and blood pressure determination in the wrist. 3. Developing a measurement system for clinical monitoring and data acquisition. 4. Making test measurements with patients in the hospital environment. 5. Conducting experiments to noninvasively determine diastolic blood pressure in the wrist based on pulse amplitude and pulse transit time, and assessing possible factors affecting the achieved measurement accuracy. This step also involved carrying out experiments to noninvasively determine systolic blood pressure in the wrist and using pulse shapes to enhance measurement accuracy. 6. Comparing the accuracy of the presented method to that of the oscillometric method. 7. Summarizing the results and assessing the feasibility of the solution.

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1.3 Organization of the thesis This thesis consists of six chapters. Following Introduction, Chapter 2 presents a description of widely available noninvasive methods of measuring blood pressure, complemented by a detailed account of them and their historical backgrounds. At the end of the chapter, a presentation is given on the classification of noninvasive blood pressure measurement devices, their accuracy requirements and methods of determining accuracy. The next chapter describes the design, construction and testing of low-power blood pressure pulse sensors and their use in the detection of blood pressure pulses and noninvasive determination of blood pressure. Next comes a description of the measurements system, comprising an automated arm cuff and a wrist sensor as well as a data acquisition and conditioning system that is connected to a patient monitoring unit and a personal computer. Experimental measurements were conducted at the Oulu University Hospital among young, healthy volunteers and elderly cardiac patients, whose characteristic blood pressure values are presented at the end of the chapter. These measurements were conducted simultaneously with two noninvasive measurements using the oscillometric and the new electronic palpation method introduced here. The chapter goes on to show the measurement results and assess the methods’ accuracies using intra-arterial blood pressure measurements as a reference. Chapter 4 presents detailed findings for the methods. Chapter 5 discusses the significance of the results, while Chapter 6 provides a summary of this work.

2 Noninvasive blood pressure measurement methods A historical overview of the different noninvasive blood pressure measurement methods will be given here together with a description of each. Most of the historical data are gleaned from Geddes’ Handbook of Blood Pressure Measurement (1991), complemented by new information put out after its publication. Particular emphasis will be placed on detailing the methods’ strong and weak points and on the achieved accuracies. Towards the end, I shall show the accuracy of a number of blood pressure measurement devices. The pressure unit used systematically in this thesis is mmHg, which, although not an SI unit, is nonetheless a globally approved blood pressure unit for historical reasons.

2.1 Counterpressure with sphygmomanometer Noninvasive blood pressure measurement methods are indirect and based mainly on measuring counterpressure. Putting a hand in a water-filled chamber, and sealing it with the wrist, allows the measurement of pressure alterations in the chamber. This pressure alteration, caused by blood pressure oscillations in the hand, depends on the static pressure within the chamber. If this pressure is increased, the pressure alteration will rise, reach its maximum, and then decrease until blood circulation in the hand stops altogether. In 1876, Etienne Jules Marey, the French physiologist, first used this method for noninvasive blood pressure assessment in humans. In the same year, Von Bash employed a water-filled bag that was connected to a mercury manometer. When this bag was pressed with increasing force against the skin over the radial artery, pulsations could be observed until, after a certain point, they disappeared. At this point, counterpressure was assumed to represent systolic pressure. Hydraulic counterpressure was subsequently replaced by pneumatic pressure. Marey designed an instrument, consisting of a rubber sleeve in a rigid cylinder, a mercury manometer and a pump. Once a finger was attached into the cylinder, weak pulsations could be observed in the mercury level. Potain improved this measurement system in 1902, by replacing the mercury manometer with a gauge and substituting water for air. In 1895, Mosso improved Marey’s measurement system by applying counterpressure on all fingers, thereby obtaining a larger signal.

17 Almost at the same time, Scipione Riva-Rocci (1896) in Italy and Hill and Barnard (1897) in England developed a cuff, which was attached around the upper arm, thus occluding the brachial artery when pressurized. In both these methods, the used cuffs were too narrow according to Von Recklinghausen (1901). In 1899, Gärtner introduced a method to determine systolic blood pressure by placing a cuff around a finger, pressing blood off from the distal side of the cuff, inflating it to a point above presumed systolic pressure, and then deflating cuff pressure. When cuff pressure reached the systolic blood pressure level, blood flushed to the finger tip. Gärtner’s method has since been known as the blush or flush method. As the pneumatic sleeve or cuff, also referred to as a sphygmomanometer, consisted usually of a rubber bladder woven inside a fabric, it enabled indirect, noninvasive arterial blood pressure measurements. Up until the present day, the cuff has been the single most important component of noninvasive blood pressure measurement devices. The cuff is usually attached around the arm on the brachial artery or, in some devices, around the wrist or even a single finger. Pressure measurements can be made by using a mercury or aneroid manometer, which have become the “gold standards” in clinical use, but are now being replaced with silicon-based pressure sensors inside automatic measurement devices, based usually on the auscultatory or oscillometric method.

2.2 Auscultatory method In his MD thesis of 1905, Nikolai Korotkoff, from St. Petersburg, Russia, presented blood pressure measurements performed on animals using a Riva-Rocci sleeve, mercury manometer and child stethoscope. First, he quickly raised cuff pressure until it stopped blood circulation on the distal side of the hand, indicated by palpating the radial artery. During the following slow pressure drop, audible sounds could be heard through the stethoscope, which was placed on the skin beyond the sleeve. These sounds were affected by the blood wave in the artery under the cuff, and were audible at 10-12 mmHg, slightly before the pulse could be palpated on the radial artery. At this point, cuff pressure is taken to indicate maximum blood pressure, while minimum blood pressure is achieved when the murmur sounds disappear. (Geddes 1991) Korotkoff sounds were corroborated by the British researchers MacWilliam and Melvin (1914) and the American Warfield (1912), who used intra-arterial pressure measured from a dog as a reference. Then in 1932, Wold and von Bonsdorff applied Korotkoff’s auscultatory method on humans using intra-arterial blood pressure as a reference. Later, the characteristic sounds heard during cuff deflation were divided into five phases on the basis of their intensity. First to publish were Goodman and Howell (1911), followed by Grödel and Miller (1943), Korns (1926) as well as Rappaport and Luisada (1944). The appearance and disappearance of sound can be used to determine systolic and diastolic blood pressure, respectively, while the cuff deflates. However, establishing the point at which sounds disappear is not always obvious; in fact, misleading readings are easy to record. Thus, a certain sound intensity level is often used to determine the point

18 corresponding to diastolic blood pressure. Also the sound amplitude maximum can be used to determine mean arterial blood pressure, as demonstrated by Davis and Geddes (1989 and 1990). The origin of the sounds has been discussed since Korotkoff’s initial findings. McCutcheon and Rushmer have provided the latest universally acknowledged explanation (1967): “An acceleration transient produced by an abrupt distension in the arterial wall as a jet of blood surges under the cuff into the distal side of the artery. This produces the first tapping sound which signals systolic pressure. It continues as cuff pressure is decreased and disappears at diastolic pressure.” Also, they mentioned that “Turbulent or eddy flow, which follows the initial jet, and produces audible sound. This factor plays little or no significance in the auscultatory technique.” It should be pointed out that the genesis of the sound will be different in the four phases; one mechanism predominating in one phase and a different one in another. Geddes (1991) summarised in his Handbook of Blood Pressure Measurement that other factors can also be pointed out, such as the rate of the pressure increase, which has a direct effect on sound intensity (Ehret 1909, Bramwell 1926 and Tavel 1969). As a result, the auscultatory method may yield erroneous results in hypertension (Rodbard 1962, 1967 Pederson and Vogt 1973). In addition, fluid velocity plays an important role, as explained by Flack (1915). Thus, a sufficient flow velocity is a precondition for the audibility of Korotkoff sounds (Rappaport 1944, Rodbard 1953, and McCutcheon 1967). Measurements based on the auscultatory method are difficult to automate, because the frequency spectrum of the different phases of Korotkoff sounds is closely related to blood pressure. When a patient’s blood pressure is high, also the recorded frequency spectrum is higher than normal and decreases as a function of blood pressure. With hypotensive patients and infants, on the other hand, the highest spectrum components can be as low as 8 Hz (Whitcher et al. 1966 and 1967), which is below the human hearing bandwidth. Normotensive subjects, in turn, require a bandwidth of 20 Hz to 300 Hz for a sufficient reproduction of Korotkoff sounds (Geddes 1991), although most of the energy of the signal spectrum is below 100 Hz. Since technology improvements have enabled efficient signal processing and calculation, a number of papers have been published on the classification of Korotkoff sounds. Cozby and Adhami (1993), for example, discovered the sub-audible, low frequency part of Korotkoff sounds and concluded that energy in the 1-10 Hz bandwidth of the total energy rises from 60% to 90%, when cuff pressure decreases from above the systolic to a level below it. This feature can be used as a thresholding algorithm for determining systolic blood pressure. Regueiro-Gómez and Pallás-Areny (1998) were able to use the spectral energy dispersion ratio to determine systolic and diastolic blood pressure with high precision: in 97% of all cases, the determined values for 15 persons were within ±1 heart beat. In ambulatory measurements, when the patient is able to move moderately, noise may become dominant, thereby spoiling the measurement. This may be avoided by using two identical microphones under the cuff, one located on the upper side, the other on the distal side. Ambient noise reaches both microphones at the same time, but the blood pressure pulse propagating through the brachial artery arrives after a time delay. This phenomenon can be used for noise cancellation as described by Sebald et al. (2002).

19 TM

Allen et al. (2004) used a Matlab -based short-time Fourier transform joint timefrequency analysis to obtain spectral information of Korotkoff sounds. Their results show that phase III has the largest overall amplitude and high frequency energy. In contrast, phases IV and V have the lowest amplitude and frequency components. Statistically significant transitions were observed between the different phases; thus, the transition from phase I to II was characterized by increases in high frequency and sound duration, the transition from phase II to III by a significant decrease in sound duration, the transition from phase III to IV by decreases in maximum amplitude, highest frequency and relative high frequency energy, and the transition from phase IV to V by decreases in the maximum amplitude and high frequency energy. They concluded that the method was able to detect the five phases also identified by an expert cardiologist, verifying that the method can be used in automatic blood pressure measurements.

2.3 Oscillometric method As discussed previously, the oscillometric method was first introduced by Marey in 1876. Erlanger (1904) further developed it and attached a Riva-Rocci cuff around the upper arm instead of fingers. Pressure oscillations were recorded on a rotating drum. This graphic recording was discarded by Pachon (1909), who used dual-dial gauges, one for presenting oscillation amplitude and the other for showing cuff pressure. In those days, it was thought that maximum oscillation amplitude indicated diastolic blood pressure (Howell and Brush 1901). It was more than half a century later that Posey and Geddes showed that that point actually corresponds to mean arterial pressure. Ramsey (1979) and Yelderman and Ream (1979) verified the finding with adults, while Kimble et al. did the same with newborn infants (1981). At around the same time, Alexander et al. (1977) demonstrated that the cuff width should be at least 40% of the arm circumference. Diastolic and systolic blood pressures can be determined using special fractions of the maximum oscillation amplitude, also known as characteristic ratios. Thus, the special fractions for systolic and diastolic blood pressure are 50% and 80%, respectively. Friesen and Lichter (1981) used these characteristic ratios to determine the diastolic and systolic blood pressures of infants, neonatal and pre-term babies with excellent results: the regression line formula was D = 0.94p + 3.53 for systolic pressure and D = 0.98p + 1.7 for diastolic pressure. Geddes et al. (1982) made measurements with animals (13 dogs) using the direct invasive method as a reference and with humans (43 adult subjects) using the auscultatory method as a reference. Auscultatory signals inside the cuff were recorded using a tiny piezoelectric microphone with a bandwidth of 30-300 Hz to record Korotkoff sounds and another with a bandwidth of 0.3-30 Hz to obtain cuff oscillations, both recorded simultaneously. For humans, the characteristic ratios were 45%-57% and 75%86% for systolic and diastolic pressure, respectively. For animals, these values varied between 43%-73% and 69%-83%. Inevitably, these ratios vary considerably and more theoretical studies are needed.

20 Drzewiecki et al. (1994) used a mathematical model to study the theory that the oscillation maximum appears at mean arterial pressure. Their model supported the hypothesis. In addition, they found the average characteristic detection ratios to be 59% for systolic and 72% for diastolic pressure. They also suggest that the systolic ratio should be lower for hypertensive patients and that the diastolic ratio should be lower for hypotensive patients. Cristalli et al. also conducted experimental studies (1992-1994) using a pneumatic system. Later, they constructed mathematical models to describe pressure distribution and the influence it has on tissue properties under the cuff. With a computer-controlled system, they demonstrated that frequent cuff inflations and deflations may significantly decrease the recorded diastolic and systolic blood pressure values. With some patients, the decrease, brought about chiefly by a decrease in Poisson’s ratio, may exceed 10 mmHg. Ursino and Cristalli (1994 and 1996) constructed a lumped parameter mathematical model which took into account compliance of the occluding cuff, pressure transmission from the cuff to the brachial artery through soft arm tissue and blood changes occurring in the collapsing brachial artery under the external load. In addition, they showed that the rigidity of the arterial wall and tissue compliance have a significant effect on the accuracy of diastolic and systolic pressure values, causing an error of 15%20%. By contrast, changes in mean arterial pressure and cuff compliance did not influence the measurement to a large degree. They also supposed that the mean arterial blood pressure can be determined as the lowest pressure at which cuff pulse amplitude reaches the plateau. Another finding by Ursino and Cristalli was that the characteristic ratios are also greatly affected by the arterial wall’s elasticity; thus, excessively elastic arteries may lower the systolic ratio by 25%-30% and this figure may rise to 80% with stiffened arteries. According to their model, arterial stiffness has a considerably weaker effect on the diastolic ratio. Typical characteristic ratio values were 46%-64% for the systolic ratio and 59%-82% for the diastolic ratio. As a conclusion, a measurement device with fixed ratios for determining systolic, mean and diastolic blood pressure may significantly overestimate them. Moraes and Cerulli (1999 and 2000) also studied the characteristic ratios using computer-controlled linear cuff pressure deflation with 10 patients and 75 volunteers, using the auscultatory method as a reference. A fixed percentile rule yielded a value of 56% for systolic and 76% for diastolic pressure. With these ratios, they got an average errors and standard deviations of error of (-0.9 ± 7.0) mmHg and (1.0 ± 6.5) mmHg for systolic and diastolic blood pressure, respectively. This format (average ± standard deviation) will be used anytime in the text if not specified in other terms. Applying an adaptive classification rule, they changed the ratios in conformity with the arm circumference and mean blood pressure, i.e., the systolic ratio was decreased from 64% to 29%, mean artery pressure was changed from less than 70 mmHg to over 150 mmHg and diastolic pressure was increased from 50% to 75%. The obtained accuracies were (1.5 ± 5.1) mmHg for systolic and (0.6 ± 5.9) mmHg for diastolic pressure. As can be seen, the standard deviation decreased slightly. In general, the deviations in the characteristic ratios recorded by Moraes and Cerulli ranged widely. Being dependent on the used ratios, the fixed percentile rule will lead to erroneous readings in many measurements. In addition, motion artefacts, the white coat effect, shivering and arrhythmia generate noise that has an adverse effect on the accuracy of the

21 readings. Lin et al. showed in a paper published in 2003 that, to minimize noise, different mathematical methods can be used, including averaging, also known as smoothing, Kalman filtering or fuzzy logic discrimination with recursive weighted regression. Colak and Isik (2003 a & b and 2004 a, b & c) utilized neural networks and fuzzy logic to classify blood pressure profiles. Because most manufacturers of automatic devices do not reveal exactly how their device determines the pressure values, the only option available to researchers is to test them with a large number of patients with different conditions, including hypo-, normo- and hypertension, arterial stiffening, heart disease, arrhythmia and so forth. Bur et al. (2000) studied the accuracy of one commercial (Hewlet Packard) oscillometric blood pressure monitor. They tested three different cuff widths on their subjects, who were critically ill patients, using intra-arterial (radial artery) pressure as a reference. The authors concluded that the device significantly underestimated arterial blood pressure and produced a high number of measurements out of the clinically acceptable range. Therefore, this type of oscillometric blood pressure measurement fails to achieve an adequate accuracy level with critically ill patients. Reeben and Epler (1973 and 1983) were the first to suggest using a modified oscillometric measurement system, where cuff pressure changes beat-to-beat on the basis of the actual mean pressure in the artery. In 1996, Jagomägi et al. presented a continuous differential oscillometric blood pressure method, which was capable of continuously measuring mean arterial pressure. The device (UT9201) used two servo-controlled cuffs around two adjacent fingers, one for mean pressure and another for slowly changing the pressure between a value higher and lower than the mean. Jagomägi’s measurements were carried out on young volunteers using the volume clamp method (Finapres) as a reference. In many of the measurements, variations in mean arterial pressure were very similar. The achieved accuracy for mean blood pressure was (-1.1 ± 5.5) mmHg at rest, (0.5 ± 6.9) mmHg in head-up tilt and (-3.6 ± 7.7) mmHg during deep breathing. Raamat et al. (1999) studied maximum oscillation criteria and found that, with contracted finger arteries, the method gives overestimated values of up to 19 mmHg. With relaxed arteries, however, the error is less evident. In their next paper (2000a & b), Raamat et al. tested accuracy during local hand heating and found no statistical error in the absence of vasoconstriction ((0.3 ± 0.3) mmHg). With peripheral vasoconstriction, on the other hand, there was a statistically significant difference ((6.7 ± 2.0) mmHg). Raamat et al. (2001) also studied whether accuracy is affected by cooling the arm locally; their results were statistically significant ((-1.5 ± 1.1) mmHg before cooling and (8.8 ± 6.3) mmHg after cooling). Jagomägi (2001) recommended that intensive vasoconstriction, which can be determined by a laser-Doppler skin flow meter, should be avoided during measurements. Further, Raamat et al. (2001) conducted a set of measurements during a rhythmical quadriceps exercise and simultaneously performed static handgrip compression using a rapidly (0.2 s) inflating and deflating occlusive cuff in the right hand. Finapres and UT9201 cuffs were fixed around the subjects’ left hand fingers. The aim was to affect peripheral vascular resistance and blood pressure, and the results showed that both devices tracked mean arterial pressure similarly. The test groups’ averaged difference was 1.2 mmHg after successive inflations and 3.8 mmHg after successive deflations.

22

2.4 Volume clamp, or vascular unloading, method Penaz invented the continuous noninvasive finger blood pressure measuring method based on a transparent finger cuff, photoelectric plethysmogram, i.e. transmitter and detector, and pressure controller unit in 1973. Molhoek et al. (1984) then tested it with 21 hypertensive patients using intra-arterial pressure as a reference. They obtained an underestimation of 6 mmHg for both systolic and diastolic blood pressure, making the method attractive for further analysis. Gravenstein et al. (1985) studied tissue hypoxia in order to assess its impairing effect on blood circulation in the finger tip on the distal side of the finger cuff. They observed an oxygen desaturation from 97% to 93.7%, recorded after applying cuff pressure for one minute. Boehmer (1987) from Ohmeda Monitoring Systems introduced a prototype device, later known as FinapresTM, based on the vascular unloading method, in which the cuff on the finger is pressurized to the same pressure as the artery. Cuff pressure was controlled by electronic, automatic adjustments so that it equalled the intra-arterial pressure at all times and pulsated with arterial pressure. These measurements consisted of photoplethysmographic measurements of finger volume; a light source and a detector located inside a transparent cuff measured blood volume alterations, i.e., blood flow. Cuff pressure was controlled by servo technique using a microprocessor to keep the blood flow stable; when the pressure inside the artery increased, external cuff pressure increased by the same amount at the same time. The system automatically corrected changes induced by smooth muscle contractions or relaxations. Factors affecting Finapres’ reliability and optimal measurement conditions were studied by Kurki et al. (1987). They measured 50 men during surgical operations using four different fingers and three different types of cuff and established that, compared to the intra-arterial blood pressure, the highest accuracy was achieved with the thumb: the observed accuracies were -4.8 mmHg for systolic, 1.49 mmHg for diastolic and 0.29 mmHg for mean arterial pressure. The correlation coefficients were 0.945, 0.884 and 0.949, respectively, indicating high accuracy. Epstain et al. (1989), however, studied obstetric patients during spinal anaesthesia and compared Finapres’ accuracy with that of other automated oscillometric methods. Differing greatly from previous studies, their results indicated an accuracy of (6.6 ± 12.5) mmHg for systolic, (3.3 ± 10.4) mmHg for mean and (7.2 ± 9.8) mmHg for diastolic pressure. Later Kugler et al. (1997) ran a series of tests with a sphygmomanometer and concluded that there were systematic differences in the results of the different methods. It must be pointed out, however, that their reference method was noninvasive and thus less accurate than the invasive method used by Kurki et al. Shortly after, Kurki et al. (1989) published a study on open-heart surgery, which demonstrated that Finapres worked well with 13 patients out of 15, before they had a cardiopulmonary bypass operation and in 10 out of 15 during the operation. They reported average accuracies of (8.3 ± 10.2) mmHg and (6.6 ± 8.7) mmHg with the correlation coefficients of 0.814 and 0.902, respectively. Since then, a number of papers have explored the accuracy of Finapres. Bos et al. (1996) and Wesseling (1996) have also defined a specific correction method for Finapres and Portapres (Shmidt 1991), in which the arterial pressure waveform recorded on a finger is filtered and bias-corrected to closely correspond to intra-brachial pressure. To conclude this discussion, it must be

23 pointed out that, as a result of extensive combined studies conducted by Silke & McAuley (1998) and Imholz et al. (1998), the accuracy of Finapres is now broadly acknowledged. More than 150 papers have been published on blood pressure measurements using Finapres, some of which are tabulated in Table I. Table 1. Comparison of the accuracy (mean values ± standard deviations) of the Finapres (also Portapres and Finometer) and intra-arterial pressure (IAP). Year

Authors

1991

Schiller Z &

Reference method IAP

I: 15 Two groups, II:16 satisfactory reliable

IAP

Not reliable during

Pash T 1991

Epstein R H

Num. Comments

et al. 1992

Bos W J

Systolic Mean Diastolic accuracy accuracy accuracy (mmHg) (mmHg) (mmHg) I:-1.6 ± 16.6, I: 0.2 ± 11 I: 4.6 ± 11.5 II:-2.7 ± 13.1 II:-2.6 ± 10.9 II: 4.7 ± 9.8 5.8 ± 11.9

7.7 ± 10.0

8.2 ± 9.8

-15.7 ± 18.8

-20.1 ± 15.7

-13.5 ± 15.7

anaesthesia IAP

12

et al.

Hypertensive and vascular disease

1994

Novak V et al.

IAP

20

One hour recording

0.84 ± 13.3

6.67 ± 5.23

1994

Triedman J K

IAP

27

Operated pediatrics

-18.6

-13.4

IAP

15

Elderly at rest

-16.8 ± 2.6

-10.8 ± 1.5

-17.5 ± 1.6

IAP brachial

28

Healthy elderly and

-3.2 ± 16.9 -3.7 ± 7.0*

-13.0 ± 10.5 -0.7 ± 4.6*

-8.4 ± 9.0 1.0 ± 4.9*

et al. 1995

Rongen G A et al.

1996

Bos W J et al.

1998

Silke B &

vascular diseased

IAP

patients 449 Combined databases

2.2 ± 12.4

2.1 ± 8.6

0.3 ± 7.9

IAP

1031 Combined databases

-0.8 ± 11.9

-1.6 ± 7.6

-1.6 ± 8.3

1 ± 10 1 ± 11*

-10 ± 8 -2 ± 7*

-8 ± 7 -2 ± 7*

-9.7 ± 13.0

-16.3 ± 7.9

-11.6 ± 8.0

-1.1 ± 10.7* 3.1 ± 7.6*

-1.5 ± 6.6* 2.7 ± 4.7*

-0.2 ± 6.8* 4.0 ± 5.6*

McAuley D 1998

Imholz B P M et al.

2002

Westehof B E et al.

2003

Guelen I et al.

IAP brachial

14

IAP brachial

37

Hypertensive, 24 h, Portapres, correction Age 41-83 Finometer, 1st correction nd

2 correction *Corrected

Finapres, Portapres and Finometer are widely used in different types of physiological measurements. For example, in 60 degree head-up tilt tests (Friedman 1990), the obtained blood pressure variation correlation coefficient was 0.98 with intra-arterial blood pressure. Hildebrant et al. (1991) and Penzel (1993) have performed a range of studies on exercise and sleep. Demonstrating that the volume clamp method can also be used in other ways, Sasayama et al. presented in 1994 findings from studies involving a cuff and a photoplethysmogram attached on the ear. The average errors were 3.7 and 2.7 mmHg for systolic and diastolic pressure, respectively.

24 Finapres devices are no longer available, since they have been replaced by another device, known as Finometer, a trademark of FMS, Finapres Medical Systems BV, Arnhem, Netherlands. The company also produces PortapresTM blood pressure monitors, which measure two fingers alternately, enabling one finger to normalize while the other is being measured. Langewouters et al. (1998) encourage physicians to use Finapres or Portapres instead of invasive methods. Currently, the latest journal paper on the topic was published by Maestri et al. in 2005. With Finometer and Finapres, they conducted continuous blood pressure measurements lasting approximately four hours on 19 cardiac patients (including patients with myocardial infarction) using intra-arterial blood pressure as a reference. Systolic and diastolic values were automatically defined for each signal. They divided the bandwidth into three bands, VLF (0.01-0.04 Hz), LF (0.04-0.15 Hz), HF (0.15-0.45 Hz) and calculated the power spectral density for each band. In addition, percentage errors were computed for all these bands and coherence and gain functions were obtained. With both devices, systolic and diastolic signals showed a high rate of errors in the VLF and LF regions. As a result, the authors concluded that the accuracy of Finometer exceeds that of Finapres in noninvasive measurements of absolute arterial pressure. However, in blood pressure variability measurements it produces worse results in most spectral parameters. When autonomic assessment is based on analyzing spontaneous blood pressure variability of pure cardiovascular origin, it may be preferable to use Finometer’s Finapres output instead of a reconstructed brachial artery waveform.

2.5 Tonometric method First presented by Pressman & Newgard in 1963, the arterial tonometer is a pressure measurement method that can noninvasively and continuously record pressure alterations in a superficial artery with sufficient bony support, such as the radial artery. It uses a miniature transducer (e.g., by Millar Instruments) or a rigid sensor array (e.g., by Colin Electronics) or a flexible diaphragm (Drzewiecki et al. 1983), which is attached on the skin above the pulsating artery. Skin and tissue located between the sensor and the array transfer pressure pulsations between them. When the pulsations reach their strongest level, the sensor is regarded as being correctly positioned. This can be facilitated by using a sensor array and selecting sensor elements with the strongest amplitude. This method requires that the sensors are closely alike in terms of sensitivity. Next, the sensor or sensor array is pushed towards the vessel using, for example, air pressure. The vessel flattens when the pressure and, consequently, the force against the artery wall increases. Arterial pressure in the top of the flattened artery’s center equals the supporting pressure, allowing the recording of an accurate blood pressure profile. If the pressure increases too much, the artery will occlude totally and the measurement will be erroneous. Thus, the hold-down pressure must be continuously controlled using stress distribution information. Furthermore, the size of the sensor or sensor element must be small relative to the artery and the sensor material must be rigid, which has led to the use of piezoelectric or piezoresistive (made of silicon) materials. Moreover, sensor arrays enable the use of motion artifact cancellation methods to improve the signal-to-noise ratio, as in the

25 piezoelectric sensor arrays presented by Ciaccio & Drewiecki in 1988 and Ciaccio et al. in 1989. The measurement method and the piezoresistive silicon sensor array were presented by Weaver et al. in 1978 and by Terry et al. in 1990. Developed in the USA in co-operation with SRI International & IC Sensors, the sensor has been marketed by Colin Electronics, Co., Ltd., Japan (Ohmori 1992) since 1992. Zorn et al. (1997) validated the tonometer monitor, known as Colin Pilot 9200, with 20 adult and paediatric patients using intra-arterial blood pressure as a reference. They achieved an accuracy of (2.24 ± 8.7) mmHg and (0.26 ± 8.88) mmHg, which slightly exceeded the allowable standard deviation of error. Since then, the Colin tonometer has been used to estimate the waveform of central aortic blood pressure (Hori et al. 1997, Chen et al. 1997, Fetics 1999) using mathematical transformation, i.e., a transfer function between a noninvasive tonometer and an invasive catheter measurement site. An autoregressive exogenous model of the Colin tonometer signal proved accurate in studies by Hori (approximately a 3% estimation error for systolic, mean and diastolic pressure) and Fetics ((0.4 ± 2.9) mmHg). Chen studied a generalized transfer function model and obtained an almost identical reading ((0.2 ± 3.8) mmHg). Nowadays, the Colin Pilot is represented by DRE Medical, Inc., USA. Another type of tonometer is based on volume changes in a fluid-filled, flexible diaphragm chamber (Bansal et al. 1994, Drewiecki 1995, Drewiecki et al. 1996). In this setup, the sensor base is made of plexiglass. A rounded rectangle-shaped channel is machined into it, and the sensor array is sealed with a thin polyurethane sheet, functioning as a flexible diaphragm. Next, the channel is filled with saline. At each end of the channel, there are two stainless steel electrodes used to inject a current along the channel length. Near the centre of the channel, four measuring electrodes are placed at equal spacing. Each pair of electrodes defines a volume compartment and the voltage across each pair is calibrated in terms of volume using impedance plethysmography. External to the tonometer, a saline-filled catheter is used to connect the channel to an electro-mechanical volume pump. A Velcro strap can be used to fix the tonometer in place. When the tonometer is attached on the skin over a superficial artery, such as the radial artery, blood pressure pulsations in it produce volume shifts inside the volume sensor resulting in an impedance change. The pressure inside the tonometer can be varied so that the strongest signal can be measured. This pressure corresponds to mean arterial pressure, MAP. Liu & Lin (2001), Wang et al. (2002) and Lin et al. (2003) have proposed a synthetic fuzzy-logic controller with three parallel subcontrollers and a model-based linear predictor for a similar tonometer (albeit with only four electrodes) designed to track the tendency of mean arterial pressure in the radial artery. (Drzewiecki 1995) In 2002, Nelesen and Dimsdale studied the accuracy of the Colin Pilot in cardiovascular reactivity testing using a brachial artery measurement (Critikon Dinamap) as a reference. They compared the results with those of Finapres and concluded that the tonometer offered low artefact rating and high accuracy. In some measurements, Finapres overestimated the results by more than 40 mmHg and showed a considerable number of artefacts (75%). On the other hand, the Pilot tracked blood pressure in the same subject without error. Recently, in 2004, Salo et al. proposed a monolithically integrated single-channel capacitive tonometer sensor chip and demonstrated its use. Developed using industrial

26 CMOS technology combined with post-process micromachining, the chip enables the design of small, portable devices with quite low power consumption (11.5 mW).

2.6 Pressure pulse transit-time method Galen (131-200 AD) was the first in Europe to note the relationship between the heart and the arteries, and he also pointed out that the arteries contain blood, not air. He ranks as the foremost sphygmologist of Antiquity, not merely because he wrote a number of books on the subject, but because his teaching on the pulse dominated clinical practice for about 16 centuries. The first attempt to represent the pulse graphically, in a form comparable with sphygmographic tracing, was made in 1540 by Struthius, who studied pulse waves by placing a leaf on the radial artery and watching its vibrations. It was only in 1553 that Miguel Serveto described pulmonary circulation, and in 1628 that William Harvey provided the first description of blood circulation. Harvey’s work established the pulse wave as a manifestation of cardiac ejection. More than 100 years later, in 1731, the Reverend Stephen Halens recorded the first invasive arterial pressure measurement. Etienne Marey, in turn, was the first to accurately record arterial pulses in humans using a sphygmograph. Later, several sphygmographic devices were devised. (Asmar 1999) It was recognized quite early that the elasticity of an artery is related to the velocity of the volume pulses propagating through it. Moens (1878) and Korteweg (1878) derived a mathematical expression for the velocity of the pulse front traveling along an artery as a function of such factors as the elasticity coefficient, the thickness of the arterial wall and the end-diastolic diameter of the vessel lumen. This is shown in the formula below, where PWV is pulse wave velocity; t the thickness of the vessel wall, d the diameter of the vessel, ρ the density of blood and E stands for Young’s modulus describing the elasticity of the arterial wall (Asmar 1999, McDonald 1974) PWV=

tE . ρd

(1)

Pulse transmission velocity was used as a clinical index of arterial elasticity by Bramwell and Hill (1922), and later by a large number of authors using different methods. In 1981, Geddes showed that diastolic blood pressure can be monitored continuously using extravascular pulse pickups and time-domain techniques to determine the pulse-wave velocity. Gallaghan et al. studied arterial elasticity with canine common carotid arteries in 1986 and concluded that the Moens-Korteweg equation provides a very good basis for predicting the increase in PWV with increasing bias pressure. Being dependent on pressure, Young’s modulus E is not a constant. The relationship is of the form E = E0eαP, where E0 is the zero pressure modulus, α is a constant that depends on the vessel, and e is 2.71828 (Geddes 1991, reading 100-103). Thus, Formula (1) can be expressed as

27 PWV=

tE 0 eαP . ρd

(2)

Geddes et al. proposed in 1981 a method where blood pressure pulses will be measured on the same artery in different locations along the vessel. In this method, the pulses become detectable, when the cuff pressure applied on the upper arm falls below systolic blood pressure. Moreover, as the pressure decreases, the pulse delay shortens until it stabilizes. This point can be used to determine diastolic blood pressure. Geddes used this method to measure anaesthetized dogs. In his paper of 1988, Okada showed that PWV correlates well with age, gender, systolic and diastolic blood pressure and serum phospholipids. He used two sets of photoplethysmographs to record pulses in the fingertip and toe tip. Later in 1996, Franchi et al. applied the ECG, a direct blood pressure measurement and photoplehysmography, PPG, to the ear lobe. Using the first and second derivative of the photoplethysmographic signal to determine pulse onset, they found a correlation between delays from the R-wave to the aortic pulse and from the aortic pulse to the ear lobe pulse. Šantić & Šaban proposed in 1995 a method similar to that presented by Geddes in 1981, but used a finger cuff and a PPG sensor placed on the tip of the same finger. When cuff pressure drops, the plethysmographic signal can be sensed at the finger tip, and its delay to the cuff pressure signal will decrease until it stabilizes. This point can be used to determine diastolic blood pressure. In 1997, Kerola et al. proposed a method which used an arm cuff with two EMFi pressure sensors under it. The delay change between signals from these sensors was to be measured by the cross-correlation method. Later on, Nissilä et al. in 1998 (paper III) and Sorvoja et al. in 2001-2005 (papers IV-VIII) proposed a method, where an EMFi sensor is used to sense radial artery pulsations and a cuff is placed around the upper arm to occlude the brachial artery. Diastolic blood pressure determination was based on either pulse amplitude change or pulse transit time, PTT, change. In 1999, Šantić & Lacrović put forward a method based on determining diastolic blood pressure using the weighted values of three methods and systolic pressure using both the oscillometric and the palpation method, which is based on sensing the first pulse in a distal sensor. Since then, pulse wave velocity measurements have attracted a great deal of attention. During the 1990s and 2000s, it has become a broadly approved method of assessing arterial stiffness, which has proven to be a good marker for predicting premature death. In 1999, Dr. Roland Asmar wrote a book which summarizes the principles of the method, its clinical applications and factors influencing pulse wave velocity. In principle, pulse waves can be measured noninvasively on the skin over the artery between two different points in the arterial tree. As seen in the formula above, the elastic modulus is dependent on pressure, allowing it to be determined on the condition that we can measure pulse wave velocity. The modulus alternates in different positions in the arterial tree depending on the vascular tonus, i.e. vasoconstriction and vasodilation, which makes it difficult to measure PWV accurately. In his book, Asmar also mentioned that pulse wave velocity depends on age, gender, genetic factors, salt intake, heart rate and blood pressure.

28

2.6.1 Photoplethysmography for the pulse transit time method Since the discovery of the Penaz -Volume clamp method and experience gained of the Finapres blood pressure measurement method, optical photoplethysmographic, or PPG, finger pulse detection has been the principal pulse detection method for many researchers. In 2000, Chen et al. proposed a method based on measuring the beat-to-beat time interval between the QRS apex in the ECG signal and the onset of photoplethysmogram in an oximeter sensor placed on a fingertip. The method was tested with 20 cardiac patients during a surgical operation using an invasive method as a reference. PTT values were calculated and divided into two bands, high (HF, 0.005-0.04 Hz) and low (LF, DC-0.005 Hz), by digital filtering. Before that, the time scale was reorganized such that one blood pressure pulse corresponded to one PTT value. The method used invasive blood pressure recordings for intermitted calibration (systolic DClevel). The estimated values for systolic blood pressure were calculated by summing HFand LF-values, and the obtained accuracy for systolic blood pressure was (0.97 ± 0.02) mmHg with the error remaining within 10% in 97.8% of the monitoring period. This result shows that systolic blood pressure changes can be tracked using PTT, if the calibration pressure can be measured regularly and if the elasticity of the arterial wall is maintained constant. In terms of determining PTT values, an important role is played by the site where the blood pressure pulse is recorded. Sensors can be attached either on the carotid and femoral, as recommended in PWV measurements, or on the radial artery and fingertip. It is obvious that highest accuracy can be obtained by using two identical measurement methods and sensors with identical signal conditioning along the same artery. In 2001, Maguire et al. proposed the use of PPG for measuring pulsations in the brachial artery and fingertip. Although the arteries are not the same, they are nonetheless in the same arterial tree, separated by some artery bifurcations. Another important factor is determining the timing point of the pulses. Pulse arrival time measurements (PAT) are widely used not only in the biomedical field, but also in laser and radar ranging, altimetry and profilometry, optoelectronic distance and dimension measurements, nuclear spectroscopy and fluorescence life-time spectroscopy, etc. Usually, one of two methods is used: discrimination of the leading edge and constant fraction (Myllylä 1976 and Vojnovic 2002). Of these, the first method uses a constant triggering level, while the second uses a constant fraction of the maximum pulse amplitude as the triggering level, 33% for instance. As a rule, PWV, PTT and blood pressure measurements determine timing points from the foot of the systolic rise of the pulse, i.e., at the end of the diastole. As the foot may not be sufficiently distinct due to noise, it may be advisable to use line or polynomial fitting and crossing-point detection. There may be erroneous readings depending on the points taken into account during fitting. Two research groups, one led by Prof. Asada at the Massachusetts Institute of Technology, USA, and the other by Prof. Zhang in Hong Kong, have explored the topic since the turn of the millennium. Their main results on blood pressure measurements will be presented in the next paragraphs.

29 Starting with Asada’s research group; Yang et al. and Rhee et al. introduced the ring sensor in 1998. It comprised a photoplethysmography and a pulse oximetry monitor in a ring, which could be attached around a patient’s finger for continuous measurements 24 hours a day. In addition, the device contained a microcontroller (PIC16C11) required to control the pulsing of the LEDs (NIR, 940 nm and red, 660 nm), data acquisition, signal conditioning and filtering. It also had a MEMS accelerometer for artifact rejection and an RF transmitter (OOK, on-off-keying) and a button-type lithium battery. In the next generation of devices (Rhee et al. 2001 and Asada et al. 2003), this single ring sensor arrangement was replaced by a double, isolating, ring system, where the inertial mass of the battery and printed circuit board could not influence the inner ring, where the LEDs and the photo diode were. This system was artifact-resistant and insensitive for ambient lighting, enabling its application to moving, continuous 24-hour heart rate monitoring for almost a month using a button cell battery. In addition, with a discrepancy of only 1.23 pulses/min in terms of rms values, it provided good accuracy. In 2005, Gibbs & Asada proposed an artifact recovery method, based on a MEMS acceleration sensor. The method subtracts the signal produced by the MEMS sensor from the corrupted PPG sensor signal with a certain delay, with the result that the output exhibits a low distortion level. Although this correction method can be used either in an additive or logarithmic model, the latter proved better, and it can also be enhanced to include the Laguerre expansion technique (Wood & Asada 2005). Shaltis et al. (2005a and 2005b) and Asada et al. (2005) designed a new version of the photoplethysmography sensor for measuring five vital signs of a patient, including temperature monitoring. This new device consists of a LED array and a photo diode array to locate digital arteries. Also, noisy PPG signals can be recovered by a 3-dimensional MEMS accelerometer and active noise cancellation. All data is transmitted by an IEEE 802.15.4 compliant transceiver. They also considered the possibility of using PPG signals to determine blood pressure, but found no stationary relationship between PPG and ABP (arterial blood pressure) over time scales greater than 20 minutes. Chan et al., members of Zhang’s group, presented in 2001 a method for noninvasive and cuffless blood pressure measurements for telemedicine purposes. In this method, ECG was used as a timing base and start signal, and PPG from a finger as a stop signal. The device included a WAP-based Internet connection. After 20 trial measurements, they achieved an accuracy of (7.5 ± 8.8) mmHg for systolic, (6.1 ± 5.6) mmHg for mean and (4.1 ± 5.6) mmHg for diastolic blood pressure using an unspecified commercial cuff measurement as a reference. Sometime later, in 2003, Zheng & Zhang published a paper describing measurements with three different types of PPG ring; convex, flat and concave. A LED and a detector were fixed on the palm-side of the ring, while the other side housed two ECG electrodes, one on the inside surface and the other on the dorsal side. A convex-type sensor, in which the LED and the detector are 1 cm apart on the palm side, proved best in compensating for motion artifacts. Lee & Zhang also proposed mathematical methods to compensate for motion artifacts, referred to as stationary wavelet transform and wavelet transform modulus maxima, achieving a remarkable improvement. Teng & Zhang used this PPG ring for blood pressure measurements on 15 young, healthy subjects. To provide reference measurements, they used an oscillometric BP-8800 device manufactured by Colin, Ltd. Measurements, taken at three different stages: rest, step-climbing exercise and

30 recovery from the exercise, indicated an accuracy of (0.2 ± 7.3) mmHg for systolic and (0.0 ± 4.4) mmHg for diastolic blood pressure. A year later, Hung et al. reported a wireless measurement concept using Bluetooth for telecommunication from a PPG ring to a mobile phone, PC or PDA device. The achieved accuracy was (1.8 ± 7.6) mmHg for systolic and (0.5 ± 5.3) mmHg for diastolic blood pressure. Poon & Zhang extended the measurements to include 85 subjects aged 57 (average) ± 29 (min, max) years, of whom 36 were males and 39 hypertensives. These measurements, conducted over an average period of 6.4 weeks, resulted in about one thousand pairs of systolic and diastolic blood pressure estimations being made. Reference results were provided by the average of results measured by a nurse using the auscultatory method and results obtained using two clinically approved automated blood pressure meters (BP-8800 Colin, Ltd and HEM-907, Omron, Ltd). Poon & Zhang reached an accuracy of (0.6 ± 9.8) mmHg for systolic and (0.9 ± 5.6) mmHg for diastolic blood pressure.

2.6.2 Other measurement methods and sensors for the pulse transit time method As Geddes says in his book of 1991, “despite the century-and-a-half of research on the development of a suitable arterial pulse pickup, none exist today that have adequate fidelity to provide the millisecond resolution needed to employ pulse-wave velocity to track changes in blood pressure. Perhaps the use of ultrasound to detect movement of the arterial wall offers the best promise of meeting the requirements for pulse detection”, the need for high resolution transit-time measurement is essential for accurate measurements. Since then, technology improvement and miniaturization have given more possibilities for sensor technology, signal conditioning and powerful real-time data processing. Multi-channel impedance plethysmography has been used to measure volume changes caused by blood volume pulsations in a limb (Risacher et al. 1992 and 1997). Initially, measurements were conducted on a patient’s leg or arm, but later also the finger has proved a good choice (Šantić and Štritof 1998). One disadvantage of the method is that a large power supply is needed to feed the electrodes. However, the average current consumption can be lowered to 0.2 mA by, for example, decreasing the duty cycle to 1:100 (Šantić and Štritof 1998). Also magnetic resonance imaging has been used for pulse detection (Mohiaddin et al. 1993). In addition, ultrasonic methods have gained wide acceptance in diagnosis, while papers dealing with pulse wave velocity measurements were first published by the Japanese researchers Kawabe et al. and Kanai et al. in 1993 and 1994. Subsequently, the method has been investigated by Stewart et al. and Giller et al. in 1994 and by Hasegawa et al. in 1997. Yet another method involves measuring pressure directly using a pressure or force sensor or a sensor array on the skin, as explained earlier in conjunction with the discussion on the tonometric method. Microphones can be used to sense sounds produced by pressure pulses or fluid flows in arteries. This type of measurement method is referred to as phonocardiography or

31 phonoendoscopy. For example, Jelinek at al (2003) used microphones on the chest and the left carotid artery. Optical methods (other than the PPG discussed above) involve fiber optic sensors and a light source/detector pair either in contact with or close proximity to the skin surface. Fiber optics can be used to sense pressure (Li et al. 1996) or skin vibrations. Interferometers have been studied by Hong in 1994 and later by Meigas et al. (2001 and 2003) and Hast et al. (2001-2003, paper IX).

2.6.3 Standards and accuracy validation In 1987, the American Association for the Advancement of Medical Instrumentation, AAMI, published a standard for sphygmomanometers, which included a protocol for evaluating the accuracy of devices (AAMI 1987, revised 1993). In 1990, a protocol was devised by the British Hypertension Society, BHS, (O’Brien et al. 1990, revised 1993) and later by the European Society of Hypertension, ESH (O’Brien et al. 2001). This was then used as the basis for the International Protocol (O’Brien et al. 2002). According to the AAMI standard, the mean difference between different measurements must be less than 5 mmHg, or the standard deviation must be less than 8 mmHg with 85% of the measurements and in the 20-250 mmHg range. Accuracy better than 10 mmHg must be achieved with 95% of the measurements. In the British protocol, accuracy falls into four grades, A to D, where A denotes the greatest and D the least agreement. These grades represent the cumulative percentage of readings falling within 5, 10 and 15 mmHg. To fulfil the BHS protocol, a device must achieve at least grade B. As the table below indicates, grades in the British protocol are less strict than in the AAMI standard. Grades are specified separately for diastolic and systolic blood pressure. Table 2. Grading criteria used by the British Society of Hypertension. Grade

Absolute difference between standard and test device ≤5

≤10

≤15

A

60%

85%

95%

B

50%

75%

90%

C

40%

65%

85%

D

Worse than C

Put together by a working group of the European Society of Hypertension, the International Protocol for Validation of Blood Pressure Measuring Devices in Adults provides a recommendation for the validation of such devices. Using a standard mercury sphygmomanometer as a reference, the protocol requires that 33 study subjects, 15 in Phase 1 and 15 plus 18 in Phase 2. Seven measurements must be performed, starting from measurements carried out by two observers (reference), which will be averaged, followed by a supervisor’s test (device under test). Four reference measurement pairs and three supervisor’s measurements must be conducted on each patient in turn. By subtracting the mean values of the obtained reference pair measurements from the supervisor’s value, we

32 will arrive at the difference. All subjects should be at least 30 years of age and may be taking antihypertensive medication. These accuracy criteria are quite similar to those of the BHS: categories for rounded values are ≤ 5 mmHg (no error of clinical relevance), 610 mmHg (slightly inaccurate), 11–15 mmHg (moderately inaccurate) and ≥ 15 mmHg (very inaccurate). The table below can be used for validation. For clinical approval, the device under test must pass all levels. Table 3. Grading criteria used by the International Protocol. Phase Phase 1

Rounded absolute difference between standard and test device within 5 mmHg

within 10 mmHg

within 15 mmHg

At least one of three

At least one of three

At least one of three

(15x3 = 45 pairs) Phase 2.1

25

35

40

Two of three

Two of three

Two of three

(33x3 = 99 pairs)

Phase 2.2

65

80

95

All of

All of

All of

60

75

90

2/3 within 5 mmHg

0/3 within 5 mmHg

2.7 Blood pressure values Blood pressure values are divided into optimal values, normal and high normal values and hypertension, which in turn falls into stage 1, 2 and 3. For being classified as optimal, systolic pressure should be less than 120 mmHg and diastolic less than 80 mmHg. This classification equates with that of the ESH and that of WHO/ISH and is based on clinical blood pressure values. Other values can be seen in Table 4 (Williams et al. 2004). Table 4. British Hypertension Society classification of blood pressure (also ESH and WHO/ISH). Category

Systolic (mm Hg)

Diastolic (mm Hg)

Optimal

< 120

< 80

Normal

< 130

< 85

High normal

130-139

85-89

Stage 1 hypertension (mild)

140-159

90-99

Stage 2 hypertension (moderate)

160-179

100-109

> 180

> 110

Stage 3 hypertension (severe) Stage 1 isolated systolic hypertension

140-159

< 90

Stage 2 isolated systolic hypertension

> 160

< 90

When systolic and diastolic blood pressures fall into different categories, the higher category should be used to classify the patient’s blood pressure level. For example, 160/80 mmHg corresponds to stage 2 hypertension (high blood pressure).

33

2.7.1 Clinically approved blood pressure measurement devices Table 5 tabulates clinically validated blood pressure measurement devices with their prices. To meet clinical criteria, the devices must achieve a minimum B grade for both systolic and diastolic measurements in the revised BHS protocol or pass the accepted criteria of the International Protocol. Table 5. Automatic Digital Blood Pressure Devices for clinical use that are also suitable for home/self assessment. (British Hypertension Society) Device / Grade

Cost (rrp) (including VAT)

A+D-767, BHS A/A

£69.99 (June 2005)

A+D-779, International Protocol

£71.36 (June 2005)

A+D-787, International Protocol

£79.99 (June 2005)

A&D UA-774, BHS A/A

£89.99

A&D UA-767, Plus, BHS A/A

£79.99

A&D UA-767PC, BHS A/A

£120.00 (Aug 2005)

A&D UA-767V, With voice to speak reading, BHS A/A

£149.99 (Aug 2005)

A&D UA-767 Plus Memory, With memory capacity to store up to 30 readings, BHS

£84.99 (Aug 2005)

A/A A&D UA-767P-BT, With Bluetooth output only available to telemedicine service

£176.25 (Aug 2005)

providers, BHS A/A A&D UA-704, BHS A/A

£39.99 (Oct 2005)

Microlife 3BT0-A, BHS A/A

£59.95 (April 2005)

Microlife 3BTO-A(2), BHS A/A

£79.95 (April 2005)

Microlife 3AG1, BHS A/A

£49.95 (April 2005)

Microlife, As easy as 123, BHS A/A

£44.98 (April 2005)

Microlife 3AC-1 International Validation

£79.95 (April 2005)

Microlife BP A100, Derivative of 3BTO-A

£64.86 (Dec 05)

Microlife BP A100 Plus, Derivative of 3BTO-A

£84.96 (Dec 05)

Omron M5-I, International Protocol

£89.95 (May 2005)

Omron 705-IT, International Protocol

£169.95 (May 2005)

Omron 705-CPII, International Protocol

£149.95 (May 2005)

Omron MX2 Basic (Same algorithm as HEM 737), International Protocol

£52.95 (June 2005)

Omron MX3 Plus International Protocol

£59.95 (June 2005)

Omron M4-I Derivative of 705-IT Omron M6

£79.95 (Dec 05) £79.99

3 EMFi pressure sensor array 3.1 EMFi sensor material Electromechanical film (EMFi), previously known as electro-thermo-mechanical film (ETMF), has a thin porous polypropylene film structure. This type of film is produced by injecting gas bubbles into a molten plastic and then extracting a tube of this gas-plastic mixture with spherical bubbles. The tube is expanded into a thin film by blowing, which produces biaxially oriented bubbles in it. Next, the film is stretched, which transforms the bubbles into flat discs with a lateral dimension of 10…100 µm and a vertical dimension of about 3 µm. The final thickness of the film is 37…70 µm depending on the type of processing it has undergone. After that, the film is permanently charged either by a plane electrode corona discharge system in a high electric field or by using electron beam charging. To provide electrodes, EMFi is metallized on both sides using one of three methods: sputtering, vacuum evaporation or gluing a metallized thin polyester film, for instance. In addition to the material itself, the existing patent also covers the manufacturing method (Kirjavainen 1987, Savolainen & Kirjavainen 1989, Savolainen 1990). The operation of the transducer is of capacitive nature: when an external force or pressure introduces mechanical compression into the film, the spatial distribution of charges within the material changes with respect to the electrode‘s layers. As a result, a mirror charge proportional to the force is induced at the electrodes. This charge can be measured with a charge amplifier or a voltage amplifier with very high input impedance. Biomedical field tests on EMFi include detecting motor activity and breathing movements in laboratory animals (Räisänen et al. 1992, Heikkinen et al. 1993), but it has also been used to measure respiration movements and blood pressure pulsations in humans (Siivola et al. 1993, Kerola et al. 1997). Furthermore, the material has been used in smart houses to monitor demented elderly people (Lekkala 1996), to recognize walkers on an EMFi floor (Pirttikangas et al. 2004) and to measure other physiological signals (Alametsä et al. 2001 & 2004, Junnila et al. 2005, Akhbardeh et al. 2005, Koivistoinen et al. 2005, Anttonen & Surakka 2005). The film can also be used as an actuator or a loudspeaker, because it undergoes thickness alterations when a high voltage is applied

35 between the electrodes (Backman & Karjalainen 1990, Backman 1990, Hämäläinen et al. 1996, Antila et al. 1997, Lekkala 1997, Antila et al. 1998, Nykänen et al. 1999, Lekkala & Paajanen 1999). With a useful spectrum extending as far as 500 kHz, EMFi may also serve as an ultrasonic transducer (Streicher et al. 2003). There are two different types of film, standard and highly sensitive, which have a sensitivity of about 40 and 200 pC/N, respectively. This sensitivity is strongly dependent on the thermal environment during processing, storage and operation time. Thus, the charge tends to decrease, if the temperature remains at over 50°C for a long period of time. Although this characteristic of the material limits its application range, the material can also be aged to stabilize its sensitivity. The properties of EMFi, including sensitivity and manufacturing process, have been widely researched and published in recent years (Paajanen et al. 1999-2002, Peltonen et al. 2000, Neugschwandtner et al. 2001, Raukola et al. 2002, Lindener et al. 2002, Gerhard-Multhaupt 2002 and Gerhard-Multhaupt et al. 2003, Wegener et al. 2002 & 2003). The results indicate that sensitivity can be enhanced by means of different gas atmospheres and pressures during corona charging and by using different gases for bubble filling. EMFi’s temperature range can be extended by developing the manufacturing processes or changing the base material. As a result, EMFi is a highly interesting alternative for applications that call for a very sensitive and inexpensive transducer material that only requires a simple amplifier. Due to the relatively large gas voids and local corona breakdowns, sensitivity in different parts of the film may vary. This could lead to problems when an application necessitates the use of a very small transducer. A case in point is a pressure transducer measuring blood pressure pulsations over the radial artery in the wrist. In this application, the transducer must be of the array type, because the correct pulse pressure waveform is obtained from a single element sitting over the artery. Signals produced by edge elements may be decayed and noisy due to motion artifacts. Array transducers based on EMFi have been produced, tested and used to measure the heartbeat rate of immobile and moving persons (Paper I and Ruha 1998). In addition, they have been utilized in non-invasive blood pressure measurements on healthy volunteers and cardiac patients. In these measurements, a cuff was applied to the upper arm and pulsations were sensed on the distal side of the wrist. Essential findings have already been published in several conference proceedings and journals (Papers III-VIII, Vieri-Gashi et al. 2000 & 2001). In blood pressure measurements of this kind, based on the electronic palpation of the amplitude and the transit time of pressure pulse waves, pulsations are detected by a wrist array transducer during the inflation or deflation of the cuff. In the deflation mode, cuff pressure is rapidly inflated over the presumed systolic pressure level and then slowly deflated under the diastolic level. The onset of pulsations corresponds to systolic blood pressure. Diastolic blood pressure, on the other hand, can be determined by the point where the delay in the pressure pulse sensed by the transducer array stops to diminish (Geddes et al. 1981, Papers IV-VIII). Though the same method can be used during cuff inflation or deflation, the latter mode is preferable. If the transducer array is dense enough to contain four elements per millimetre, it can be used as a tonometer type of blood pressure transducer array. In this array, the transducer element that is located over the central part of the artery measures interior pressure with a high degree of accuracy, dependent mostly on the hold-down pressure and

36 the accuracy of each transducer array element. In addition, this type of measurement also requires that the transducer’s bandwidth starts at DC. (Geddes 1991)

3.2 Preparation of blood pressure sensor This study made several modifications to the existing practice of constructing small-sized transducer arrays. Commonly, transducers are made separately and connected to an amplifier unit with lengthy transfer lines made of Kapton™ PCB. Such transfer lines, however, increase input capacitance, which serves to decrease the amplified signal. In the experiments reported here, the transducer and the transfer lines to the amplifier were both made of flexible Kapton™ printed circuit board (PCB). The equivalent circuit for EMFi consists of a charge generator in parallel with a leakage resistor and a capacitor. As transfer line capacitance and amplifier input capacitance are also parallel, the amplifiers must be placed as close to the transducers as possible. A second modification involves exchanging charge amplifiers for CMOS voltage followers with high input impedance. These amplifiers are simple and do not take up much room. The transducer array was made by gluing a Kapton™ plate on a plastic base component using a double-sided adhesive produced by 3M. This plate served as the ground plane and the second electrode for the transducer. Next, the EMFi material was glued on top of this plate using the same tape. The following, similarly attached, layer comprised a ground sealed eight-element transducer electrode plate (Kapton™ PCB). The dimension of the sensitive area of the transducer arrays was approximately 7x11 mm, as seen in Fig. 1, which shows the device in its natural size from three different angles. Transfer lines were soldered to a standard PCB containing the amplifiers. This configuration has a transfer line capacitance of ~10 pF, paralleling that of the transducer elements, and its leakage resistance lies in the TΩ range.

Operational amplifiers

Fig. 1. Eight-element transducer array with EMFi as pressure sensing material. (II)

Such transducer arrays were used to study the sensitivity of sensors both as a function of temperature and of static and alternating pressure amplitude. The pyroelectrical behaviour of EMFi manifests itself in offset voltage drift. This offset voltage is affected by the

37 permanent charge of the EMFi material loaded in the transducer, the leak resistance of the transfer lines and electrodes and the input bias currents of the operational amplifier. Fig. 2 a) presents an ideal operational amplifier with electrostatic discharge (ESD) diodes in its input, which is common practice for amplifiers with high input impedance. A mismatch in the properties of these diodes causes an input bias current. If the diodes are exactly similar, a current is generated only from the positive to the negative power supply pins, and does not affect the input bias current and offset voltage. A mismatch, on the other hand, produces a potential that deviates from zero. Fig. 2 b) presents these currents as a function of potential at two different temperatures. When the temperature rises, the offset voltage of the unconnected amplifier can be located at the crossing point of the current curves. Thus, a change in temperature can produce an offset voltage drift. I2

I1

U

T=T1>T0 T=T0

V1 Uin

V2

Uout

+

Uout

Uin

-

-U

-1.5 V

a)

+1.5V

b)

Fig. 2. ESD diodes (V1 and V2) in the input pins of the operational amplifier, a), and their influence on bias currents at two different temperatures, b). (II)

When the transducer applies a very low frequency input signal (equal to Uout), the bias current increases. The bias current to and from the transducer equals the difference between these curves at the voltage value Uout. In addition, elevated temperature increases the bias current and may decrease the amplitude of the low frequency signal: the operational amplifier draws charges to itself. Another important consideration relates to the sensitivity of the different elements. Quality variations in the EMFi material and potential gluing faults may produce sensitivity variations. Such variations are also produced during storage, particularly by alterations in temperature. Other important factors in this respect include pressure distribution and cross talk between different channels (array elements). Fig. 3 a) shows the transducer array, while Fig. 3 b) displays a part of the same array in magnification. The entire sensor is well shielded to avoid noise. Instead of soldering, the ground electrode can be glued with an electrical connective glue to avoid introducing a temperature shock in the EMFi. As the sensor is not in direct skin contact, arterial pressure is mediated to it by means of plastic heads of exactly the same size as the transducer element, i.e., 2 mm x 0.6 mm with a 0.4 mm gap between them.

38 Soldering or glue

Kapton

Lead-through

Kapton foil electrodes

EMFi film

Operational amplifiers

a)

with

ground

3M double side adhesive tapes

Printed circuit board with transducer array electrodes

b)

Fig. 3. Improved version of the EMFi transducer array used to study sensibility and crosstalk between different elements. Fig. b) is a magnification of Fig. a). (II)

3.3 Measurement setups for the blood pressure sensor Two kinds of pressure generation modules were developed to measure the responses of the transducer arrays. The first one comprised a computer-controlled electromagnetic pump capable of producing a pressure pulse with a pre-programmed wave shape. The system was quite complicated; a PCB (sitting in a personal computer) was used to drive power circuits in an outside module feeding an electromagnetic membrane pump. This pump was attached to a metallic chamber with a plastic cover. The cover contained a cavity, which fitted exactly the transducer array described in Fig. 4. The chamber was filled with water, and a silicon rubber was used to isolate the water from the transducer array, see Fig. 4. In addition, the chamber housed a pressure transducer (Sen Sym PS 15GC), whose signal served as feedback-signal to the PCB. After a few pulses, the system adapted to the desired signal and produced exactly the desired waveform. This measurement used only a sinusoidal one-Hertz signal.

39 Transducer array with amplifiers Plastic cover

Rubber membrane Chamber Pressure chamber base filled with water Fig. 4. Transducer array mounted on the pressure chamber, which produces a sinusoidal one Hertz signal using a membrane pump. The membrane pump was controlled by a computer system. Also the temperature inside the chamber was controllable. (II)

As this pressure generation system was incapable of producing high frequency signals, another pressure generator had to be used for this purpose. The other generator, consisting of a piezoelectric actuator (Physic Instrument P-840.60), was used to generate sinusoidal signals for one transducer array element at a time. Contact pressure was measured from a piezoelectric actuator by means of a force transducer (Honeywell Micro Switch FSG-15N1A). The size of the contact head equalled that of the transducer element, 0.6 mm x 2.0 mm, which enabled the investigation of cross talk between the different transducer elements. In every measurement, a personal computer with a National Instruments data acquisition board (AT-MIO16 or DaqCard-700) was used for file readouts. The measurement system is illustrated in Figs. 5 and 6.

40 Oscilloscope

Signal generator

Power source

Force sensor amplifier Piezo actuator Force sensor Test head

EMFi test piece with amplifiers

Adapter card

Data acquisition board

Fig. 5. Measurement arrangement for measuring the second set of transducer arrays. The force sensor detects the sinusoidal force generated by the piezo actuator. Both this and the signal from the EMFi transducer are amplified and then recorded by a personal computer (PC) using National Instruments data acquisition board. (II)

41 Piezo actuator Force sensor Test head

Translation stage

Aligning support

PCB mounting clips

Aluminum base

Fig. 6. Magnified picture of the aligning system presented in Fig. 5 to measure the sensitivity of the EMFi material. A micro positioning translation stage is used to move the test head just above the sensor element under test. The test head is aligned exactly on top of the element. (II)

3.4 Sensor measurements Firstly, the step response of the transducer was measured using the pressure chamber at room temperature (22 °C). Fig. 7 presents the step response signal with the time constant of 1 hour and 10 minutes. In this test, the sensors covered an area of 5 mm2 and the operational amplifier was TLC2272. Since the time constant yields 38 µHz as the lower limiting frequency, the sensor material can be considered as suitable for biomedical applications. It must be noted, however, that the time constant is strongly dependent on temperature and the amplifier’s input bias current.

Amplitude (percentage of maximum)

42 100 80 60 40 20

τ

37%

0 0

1000

2000

3000

4000 Time (s)

5000

6000

7000

8000

Fig. 7. Pressure step response of the EMFi transducer using TLC2272 as operational amplifier at room temperature (21 ºC). (II)

Secondly, the temperature behaviour of the transducer was studied by placing a sensor in a temperature cabin, while the temperature was decreased from 40°C to 0°C by 5°C steps. Fig. 8 shows the offset voltages as a function of time. As can be seen, EMFi exhibits pyroelectrical behaviour. The film produces upward spikes, but the bias currents drive the voltage down. Consequently, when a sensor based on EMFi is employed to detect sub-Hertz pressure alterations, temperature should be maintained at a stable level near room or body temperature.

Offset voltage (mV)

1200 700

40

35

30

25

20

15

10

5

0

200 -300 -800 0

200

400

600 Time (min)

800

1000

1200

Fig. 8. Offset voltages of the eight-element transducer array as a function of time with eight temperature steps. (II)

This pyroelectric behaviour was further studied by decreasing the temperature linearly during a one-week period. The thus obtained offset voltages are presented in Fig. 9. As the figure shows, the offset voltage is almost zero between 25°C and 40°C. This indicates that the sensor material is suitable for biomedical applications in which the sensor is either very near or in contact with skin and thus near the body temperature. Nevertheless, when the sensor is in actual skin contact, problems may arise due to humidity evaporating

43

Offset voltage (mV)

from the skin. Because the transducer is capacitive by nature, its electrodes must be properly sealed to prevent humidity from affecting the edges of EMFi’s electrode interface. 1000 0 -1000 0

5

10

15

20

25

30

35

40

45

-2000 -3000 -4000

Temperature (°C)

Fig. 9. Offset voltages of the EMFi transducer array elements as a function of temperature. (II)

Since the characteristics of the operational amplifier evidently affected the measurements, the EMFi material was tested by resetting the amplifier’s input electrode to a positive or negative supply voltage and taking a readout when the voltage was stabilized. Also the other electrode was connected to the positive or negative supply voltage instead of zero voltage as usual. The influence of the voltage Ue produced by the other electrode is clearly indicated by several readings at different temperatures in Fig. 10, whereas the influence of the reset voltage was very small. In a further test, the transducer was replaced with a 10 pF polystyrene capacitor, see Fig. 11. As the leakage resistance of the capacitor roughly equals that of the EMFi material, Fig. 11 presents only the behaviour of the operational amplifier. The test shows that the EMFi measurement differs considerably from the capacitor measurement. The characteristics of EMFi seem to dominate over those of the amplifier at higher temperatures. According to Figs. 10 and 11, the offset voltage can be compensated for by applying opposite polarity voltage to the “ground” electrode of the EMFi transducer by means of an integrator, for example.

Offset voltage (mV)

44 1500 1000 500 0

Ue=+1.5V ,reset=+1.5V Ue=+1.5V ,reset=-1.5V Ue=-1.5V ,reset=+1.5V Ue=-1.5V ,reset=-1.5V

-500 10 -1000 -1500 -2000

15

20

25

30

35

40

45

50

Temperature (°C) Fig. 10. Offset voltages as a function of temperature when different voltages are applied to the “ground electrode” of the EMFi transducer and output pins are reset with different voltages. (II)

Offset voltage (mV)

0 -300

10

15

20

25

30

35

-600 -900

40

45

Ue=+1.5V ,reset=+1.5V Ue=+1.5V ,reset=-1.5V Ue=-1.5V ,reset=+1.5V Ue=-1.5V ,reset=-1.5V

50

-1200 -1500 Temperature (°C)

Fig. 11. Offset voltages as a function of temperature when different voltages are applied to the electrodes of a capacitor and reset. (II)

The sensitivity of the transducer array was measured with a pressure pump chamber. Fig. 12 presents the sensitivities of the eight elements as a function of temperature using a one-Hz pressure signal with a sinusoidal shape. Although the sensitivity of the various elements varied considerably, this variation appeared to be independent of temperature. Next, a set of similar measurements was carried out, but this time sensitivity was measured as a function of alternating pressure and static pressure. In the alternating pressure measurements, static pressure was 100 mmHg and alternating pressure varied from 10...180 mmHg in 10 mmHg steps. In the static measurement, on the other hand, alternating pressure was 100 mmHg and static pressure ranged from 10...130 mmHg. Being almost identical to those in Fig. 12, the results will not be presented here. The average sensitivity of the six transducer arrays that were involved in these measurements was 6.4 mV/mmHg ±18%. Variation in the sensitivity of the different elements in each array was smaller (5% to 10%).

Sensitivity (mV/mmHg)

45 10 8 6 4 2 0 25

30

35 Temperature (°C)

40

45

Fig. 12. Sensitivity of the transducer array as a function of temperature. (II)

A second set of transducer arrays was studied by investigating cross talk and quality (sensitivity deviation) in different parts of a high-sensitivity EMFi sheet. To stabilize its quality, the sheet was first aged by the manufacturer. Then the sheet, measuring 60 cm x 85 cm, was divided into 4 x 5 equal parts. Little sections were cut out of selected parts in the horizontal and vertical direction to be used as sensors. The transducer arrays were made separately for each PCB and measured at the one-Hertz sinusoidal force. Every measurement was made four times and then averaged. The results of this set, totalling 800 measurements, are presented in Table 6. Due to aging, the sensitivity of this transducer array set was much lower than that of the previous set, particularly around the edges of the sheet. Furthermore, the standard deviation of this set was quite high, indicating that the transducer material is potentially inadequate for certain measurements, such as measuring the heart rate of a jogger (Paper I, Ruha 1998). Table 6. Quality study on the EMFi material.(II) Cut type

Couples

Average sensitivity

Standard deviation

(mV/mmHg)

(%)

Horizontal

13

2.19

±11.3

Vertical

12

2.11

±16.0

Sensitivity was also analyzed as a function of static and dynamic pressure. Compared with the previous set, this set used considerably higher pressure values. Presented in Figs. 13 and 14, the results point out that, in contrast to the previous set, sensitivity decreased when static pressure increased. On the other hand, the transducers’ sensitivity to alternating pressure was stable, which is consistent with the result of the previous set.

Sensitivity (mV/mmHg)

46 3.5 3 2.5 2 1.5 1 0.5 0 0

500

1000

1500

2000

2500

3000

Static pressure (mmHg)

Sensitivity (mV/mmHg)

Fig. 13. Sensitivity of the eight-element transducer array as a function of static pressure at sinusoidal alternating pressure of 300 mmHg. (II)

3.5 3 2.5 2 1.5 1 0.5 0 0

200

400

600

800

1000

1200

1400

Alternating pressure (mmHg)

Fig. 14. Sensitivity of the eight-element transducer array as a function of alternating pressure at static pressure of 700 mmHg. (II)

The standard deviation of sensitivity measurements can be improved by laminating two or more films on top of one another. Hence, five transducer arrays were made by laminating two EMFi layers. This method produced only a slight improvement in sensitivity, but had a marked effect on the sensitivity deviation between the elements, resulting in 2.62 mV/mmHg ± 7.7%. To measure cross talk, two couples consisting of two types of transducer array were made by adhering a thick (200µm) or a thin (70µm) aluminium film on an EMFi film, functioning as a ground electrode. Table 7 and figure 16 show the average values for the sensitivity, deviation and cross talk of all these transducer types, including also single and sandwich-type transducer arrays. As can be seen, with 3.34 mV/mmHg ± 5.1%, thin aluminium produces the best sensitivity and lowest standard deviation and cross talk values.

47 Table 7. Sensitivity and standard deviation in sensitivity for different sensor types. (II) EMFi layers and electrode material

Sensitivity

Standard deviation of sensitivity

(mV/mmHg)

(%)

One layer, Copper on Kapton

2.1

13.6

Douple layers, Copper on Kapton

2.6

7.8

One layer, thich aluminium

1.5

7.8

One layer, thin aluminium

3.3

5.1

Cross talk (%)

100 80 60 40 20 0 One layer, Kapton

Douple layers, Kapton

Thick aluminium

Thin aluminium

Fig. 15. Average cross talk values of all EMFi transducer array types. The black columns are the signal of the transducer element, where the force is focused and the others are proportional signals in the neighbouring elements. (II)

4 Blood pressure measurements 4.1 Measurement setups The measurement device consisted of a standard 13 cm cuff, a wristwatch-type fourchannel pressure transducer array, an invasive catheter (Ohmeda), an amplifier, a filtering unit and an automatic pressure-controlling unit. A lap-top computer equipped with a data acquisition board by National Instruments (NI Daq 700) was used to measure the outputs of the amplifiers. Signals were sampled at the 100 Hz frequency. Based on electrothermo-mechanical film (Kirjavainen 1987, Ficher et al. 1990), the transducer array was specifically designed for detecting pulsations in the radial artery (paper I & II, Sorvoja 1998, Ruha 1998, Kokko 1999). The amplifier unit was used to amplify and band-pass filter cuff pressure signals (1-15 Hz, Butterworth filter response, fourth order high pass, second order low pass) and signals produced by the sensor. These measurements were made twice, in supine and sitting position, during inflating and deflating cuff pressure with a one-minute follow-up recording period between them. The next measurements were based on signals produced by the DATEXTM patient monitoring system and another, self-made, blood pressure measurement device. In addition to the device described above, this one also comprised an MSP430TM-based microcontroller unit for determining blood pressure. Using 64 kBytes, the measurement and blood pressure determination algorithm was coded in the microcontroller manufacturer’s assembly language (~1900 rows text). The compiled program used 512 bytes of RAM and 3970 bytes of ROM. Cuff pressure was inductively transmitted to the microcontroller unit by transmitter-receiver pairs designed by Polar Electro Ltd. Pressure information was based on time-interval measurements, with one pulse a second corresponding to zero pressure and two pulses a second to a pressure of 256 mmHg. Also the sensor’s signal band was slightly different from that of the first measurement system: four-element transducer signals were amplified and filtered using a first-order band-pass filter (1.7 Hz-11 Hz) in a full custom integrated circuit (Ruha 1998). A connection from the DATEXTM device provided an ECG signal and each patient’s radial and pulmonary intra-arterial blood pressure. An automatic pressure-controlling unit, connected to the laptop computer, started cuff pressure inflation and sent all pressure data wirelessly to the

49 microprocessor unit. Designed to assess the functionality of the whole system, the microprocessor unit contained the same blood pressure detection algorithm as the laptop computer. The data acquisition rate of the recording system was 100 samples/second. This system is illustrated in Fig. 16, with the connection cable to the patient monitoring system missing.

Fig. 16. Second measurement system. From left to right: self-controlled pressure pump unit, amplifier and band-pass filter unit, MSP430-microcontroller unit, cuff for adults with an arm circumference of 24-33 cm (Speidel & Keller), aneroid pressure metering device with a pump for pressure calibration and lap-top computer equipped with the National Instrument DAQ card 700. In the lower right hand corner is the four-element wrist sensor used to detect pressure pulses in the radial artery.

4.2 Study subjects The first set of measurements was carried out at the Oulu University Hospital during 1997. Most of the study subjects, 16 in all, were healthy volunteers. In addition, seven elderly patients were measured. It soon became apparent that the blood pressure signals of the two groups differed in shape and amplitude. At that time, the blood pressure determination algorithm focused on detecting changes in pulse amplitude, not in the recorded delays near the diastolic pressure level. Paper III describes the results of measurements made on healthy persons, and their characteristics are shown in Table 8 (average values and standard deviations). An invasive catheter (Ohmeda) was used as a reference blood pressure method. It can be seen that both the men and the women were

50 normal weight, and that their blood pressures were in the normal or high normal range. The width of the cuff was 13 cm. Table 8. Study subjects (healthy volunteers). The values are average values ± standard deviations or range . (VI & VIII) Item n

Men

Women

12

4

Age, years

26.2(20 − 33)

30.8 (27 − 34)

Height, cm

180.8 ± 5.6

170.0 ± 2.4

Weight, kg

79.4 ± 10.1

63.5 ± 6.0

BMI, kg/m2

24.2 ± 2.4

22.0 ± 1.9

Arm circumference, cm

31.6 ± 2.6

28.3 ± 2.4

Blood pressure measurements, n IA systolic BP, mmHg

98

23

129.1±11.2

122.3 ± 10.1

IA mean BP, mmHg

87.3 ± 6.5

83.5 ± 5.7

IA diastolic BP, mmHg

67.4 ±5.0

63.7 ± 5.3

Heart rate, bpm

66.2 ±11.3

69.8 ± 8.1

Taking about 5 months, the next set of measurements was conducted on elderly cardiac patients at the Oulu University Hospital in 1998-1999. Also in these measurements, reference readings were provided by the invasive method with the signal obtained in analog form directly from the patient monitor’s output. In the end, three to five measurements on 51 patients were saved on the computer for further analysis. Unfortunately, a number of measurements had to be rejected due to inadequate recording (missing arm circumference) or insufficient cuff width. As the cuff width in all measurements was 13 cm, the study subjects’ arm circumference had to be within 24-33 cm to ensure adequate accuracy (Geddes 1991, manufacturer’s claim). Table 9, tabulating the characteristics of these eligible patients, shows that their arm circumference was appropriate and that the invasive blood pressure values were within the optimal normal range. It has to be pointed out that the International Protocol had not been implemented at the time of the study, and could therefore not be followed. In addition, we did not use a noninvasive method as a reference, but an invasive one, but this is not a problem, as invasive methods invariably yield more reliable results. Also the number of study subjects (16 healthy and 20 patient) exceeded the requirements of the International Protocol (33 in all) making the study statistically valid. Of course, both sexes should have been represented in equal numbers.

51 Table 9. Test subjects (cardiac patients). To provide a single value for each patient, the intra-arterial (IA) blood pressure (BP) values given are average values of the measured IA diastolic, mean and systolic BP values (average values ± standard deviations or range). (VI & VIII) Item Number

Men

Women

14

6

Age, years

67.8 (59−75)

65.2 (49−75)

Height, cm

168.0 ± 8.1

163.0 ± 6.6

Weight, kg

71.4 ± 11.8

74.1± 10.0

Body mass index, kg/m2

25.4 ± 4.4

28.0 ± 4.6

Arm circumference, cm

32.3 ± 1.0

32.7 ± 0.5

Bypass operation

13

4

Valve operation

3

2

Blood pressure measurements, n

40

13

114.8 ± 21.3

118.3 ± 19.9

IA systolic BP, mmHg IA mean BP, mmHg

77.8 ± 9.2

79.6 ± 14.8

IA diastolic BP, mmHg

59.6 ± 6.1

59.6 ± 14.5

Heart rate, pulses/min

87.2 ± 13.5

81.5 ± 5.4

4.3 Blood pressure determination in the electronic palpation method As shown in Fig. 17, systolic blood pressure can be easily determined at the point where the last (or first with deflating cuff pressure mode) radial artery pulse appears in the wrist transducer. We used an array type of transducer to facilitate the placement of the wrist transducer, as the array element with the strongest pulsation is easy to pick out. The figure presents the IA blood pressure signal, band-pass filtered signal and electronically palpated (EMFi sensor) signal at increasing cuff pressure levels. The filtered IA blood pressure signal has exactly the same bandwidth as the electronically palpated signal. Surprisingly, the palpated signal does not begin to decrease after the point at which cuff pressure exceeds diastolic blood pressure. In fact, the signal amplitude slightly increases, which is why the point where the amplitude signal begins to decrease cannot be used to determine diastolic blood pressure. Consequently, diastolic pressure is determined at the point where the pulse delay begins to increase, and systolic blood pressure at the point where the last pulse is detected. The cuff pressure at these time points corresponds to diastolic and systolic pressure.

High-pass filtered Intra-arterial and intra-arterial cuff pressure (mmHg) pressure (mmHg)

52 150 100 50 0

5

10

15 Time (s)

20

25

5

10

15 Time (s)

20

25

5

10

15 Time (s)

20

25

20 0

-20

High-pass filtered signal of EMFi pressure sensor

-40 20 0 -20

Fig. 17. Determination of systolic and diastolic blood pressure in the electronic palpation method.

Diastolic blood pressure determination is based on pulse transit time change. When pressure in the cuff exceeds the diastolic pressure level, pulses on the distal side of the cuff will be delayed. To accurately measure the point where this delay begins requires an accurate timing base. The best timing reference is afforded by the radial artery of the other hand, and, if appropriate data processing is performed, the time difference can be easily determined. Standard methods, such as the leading edge or constant fraction method, can be applied to the determination of diastolic blood pressure. Preliminary calculations with MatlabTM showed that the leading edge method is not accurate due to variations in pulse amplitude and profile. Therefore, the constant fraction method was applied to the cardiac patient group in the 20% to 60% range using 5% steps as the trigger level. It was shown that, when the triggering level was between 30% and 45%, the average diastolic blood pressure error remained stable at the 2.5 mmHg level. Also the standard deviation of the errors was calculated for the whole patient group. The average of standard deviation as a function of triggering level was shaped like parable opening upwards and reaching its lowest point at the 35% level. These findings are presented in Figs. 18 and 19, using three different determination methods. The peak of the first derivative, varying from 22% to 50%, had an average level of 37%, which implies that to minimize error, timing should be based on the level with the fastest rising or sloping signal. This leads to the conclusion

53 that it is advisable to use derivated signals of blood pressure pulses in the right and left hand. In this situation, signals can be measured invasively using a catheter and noninvasively using a pressure sensor array. Diastolic error (mmHg)

4,0 3,0 2,0 1,0 0,0 -1,0

20

25

30

35

37

45

50

60

method 1 method 2 method 3

-2,0 -3,0 Triggering level (%)

Standard deviation of diastolic error (mmHg)

Fig. 18. Averages of diastolic blood pressure error with the cardiac patient group as determined by three different methods.

12.0 10.0 8.0

method 1

6.0

method 2

4.0

method 3

2.0 0.0 20

25

30 35 37 45 Triggering level (%)

50

60

Fig. 19. Standard deviations of diastolic blood pressure error with the cardiac patient group as determined by three different methods.

Figure 20 presents a flowchart with typical wave shapes for calculating and determining the time delay between pressure pulses from both hands. Firstly, IA blood pressure and EP signals are filtered, and the transducer element with the strongest amplitude in the EP transducer array is chosen. After that, the signals are interpolated by a factor of ten to obtain a better timing reference. Both signals are then derivated and the time difference is determined for each pulse on the basis of correlation calculations. The gained signals are then filtered using three-point moving median and average filtering. Next, the signals are interpolated so that every cuff pressure integer value has a corresponding pulse delay value. Specific “triangle conversion”, described later, is performed at the following stage to find the cut-off point for line fitting, which is used to establish the point, at which the pulse delay begins to increase. This point indicates the diastolic blood pressure value.

54

IABP (catheter)

EP sensor array

Cuff pressure

Software BP filtering (1-14 Hz)

Hardware BP filtering (1-14 Hz)

Moving average filtering

Extra filtering

Extra filtering

Interpolation

dp/dt

dp/dt

Interpolation

Interpolation 1

Time difference determination based on cross-correlation calculations of the signals

0.8

0.6

0.4

0.2

0

-0.2

-0.4

-0.6

-0.8

0

200

400

600

800

1000

1200

1400

1600

3 points moving median filtering 3 points moving average filtering Interpolation “Triangle conversion “ Line fitting Crossing point detection Diastolic blood pressure determination Systolic blood pressure determination

Fig. 20. Flowchart diagram for the electronic palpation method. (VIII)

Since the EP blood pressure signal in the radial artery has undergone analogue hardware filtering, the IA pressure signal must be filtered similarly. Further, to eliminate

55 noise that is coupled to the sensor signal from the radial artery, we used additional software band-pass filtering. Owing to the 100 Hz sampling rate used in data acquisition, the data signals were interpolated for better timing accuracy. According to our experience, an adequate resolution level is achieved at a ten-fold sampling rate. After interpolation, a derivated signal is calculated, and the time difference of these signals is worked out on the basis of correlation calculations, i.e., the best correlation corresponds to a situation where the signals overlap one another exactly. Having clearly and accurately determined the time difference, the next step involves locating the point where the pulse delay begins to increase. This turning point can be accurately determined by means of “triangle conversion” after median and moving average filtering and interpolation. In this conversion method, all time values are recalculated to form a triangle, and line fittings are used to establish the accurate turning point. To this end, the time values have to be inverted to obtain decreasing values, and Equation (3) can be applied to attain new values Tnew (i)=Told (i)-

( Told (1)-Told (n) ) (i-1) n-1

-Told (n) .

(3)

In this equation, Tnew(i) is the new time value for the index (i), Told(i) the old value and (n) the index value for the last measured time value. In Fig. 21, Fig 21.1 illustrates the increase in pulse delay during the measurement shown in Fig. 17. The pulse time difference values is then inverted (Fig 21.2) and filtered using three-point moving median and average filtering (Figs 21.3 and 21.4). In Fig 21.5, the data is interpolated and in Fig 21.6, “triangle conversion” is performed to find the diastolic blood pressure. The painted dark areas corresponds each others. Line fitting was used to locate the point at which the line crosses the 100% level. This point correlates to diastolic blood pressure; see the arrow in Fig. 21.5. This algorithm provided the value of 67 mmHg for diastolic blood pressure, while the intra-arterial value alternated between 67 and 72 mmHg during the measurement. The last detected pulse corresponds to systolic pressure and the obtained value, 157 mmHg, is within the systolic pressure range, 152 to 162 mmHg. As seen, the line fitting ranges are clearly defined by the turning point (highest value) and, after that, the crossing point value of the fitted bolded line and the 100% level line can be calculated. This point corresponds to diastolic blood pressure in the low-pass filtered cuff pressure.

56

100

5 10

0

3.

-100 0 50

5 10

ms

ms

-50 0 100

0

5.

-50 0

50

0

ms

1. 15 20 s

25 30

-100 0 50 0

ms

0

15 20 s

25 30

100 mmHg

150

2.

5 10

15 20 s

25 30

15 20 s

25 30

4.

-50 0 100

5 10

50

6.

%

ms

50

6.

0 0 20 40 60 80 100 120 140 mmHg

Fig. 21. Pipeline determination of diastolic blood pressure from the time difference between the filtered and derivated signals of intra-arterial and electronically palpated blood pressure pulses. 1) Time difference, 2) inverted time difference, 3) 3-point moving median filtered time difference, 4) 3-point moving average filtered time difference, 5) interpolated time difference and 6) “triangle conversed” time difference. (VIII)

Pressure pulse wave shape, for example augmentation, can be used to assess the level of arterial stiffening. The problem is that augmentation depends on the systolic pressure level and appears and disappears with pressure fluctuations. Fig. 22 presents a typical waveform for an augmented pressure pulse. Systolic pressure is boosted some 20% over the original value, due to reflections from the periphery. The manifestation of this augmentation is easily tested by measuring the rising time from 10% to 90% of the pulse pressure (Trise), or the time from the 50% level to the top level (T2) or the pulse above the 80% level (T1). The original systolic pressure can be defined by the “systolic turning point” of the first derivative signal after the peak (Fig. 23). Unfortunately, the signals are often weakened by noise. The second derivative can also be computed, but subsequent signals are too noisy. It was established that the percentage of the first derivative peak of the pulse pressure is well suited to describing arterial stiffness. Hence, if the pressure slope is highest shortly after the foot of the pulse (diastolic value), the patient is likely to suffer from stiff arteries. In young and healthy persons, however, this level is near the 50% level

57

Intra-arterial pressure (mmHg)

of the pulse pressure. Another way of describing the waveform is the percentage of the pressure pulses’ mean pressure level, which can be easily computed. Also pressure pulse transit time was defined, followed by a calculation of pulse wave velocity (PWV). Normally, PWV is noninvasively measured using pressure pulses between the carotid and femoral artery, followed by a determination of the distance between the measuring points (Asmar et al. 1995 and Asmar 1999). In this study, time was determined from the ECG’s R-spike to the end of diastole, when the pressure is just about to rise again. Distance from the heart to the wrist was defined afterwards by multiplying the patients' height by the coefficient of 0.43, an empirically established mean value for normal young people (according to Dr. Jukka Hast). This value shows an individual variation of about ±10%, producing a corresponding error in the determination of PWV.

150

100

100% 90% 80%

T1

50%

T2

10%

Trise

0% 50

0 2500

3000

Time (ms)

3500

4000

Fig. 22. Blood pressure signal and specific time values producing a numerical signal description. (V)

58

200

Pressure (mmHg, a.u.)

Systolic pressure level 150 100

Mean pressure level First deriv. peak level Diastolic pressure level

50 0 -50 2500

3000

Time (ms)

3500

4000

Fig. 23. Blood pressure signal (upper) and its first derivative multiplied by five (below). (V)

Other factors were also tabulated, including the visually detected existence of augmentation, average of intra-arterial systolic mean and diastolic pressure, age, weight, height, cardiac output and peripheral and blood temperature.

4.4 Accuracy of the electronic palpation method Systolic pressure was determined visually from the last palpated pulse, while diastolic blood pressure readings were obtained by the time delay change method described above. Reference values were provided by the average IA systolic and diastolic pressure. The corresponding accuracies are presented in Table 10. For the healthy group, the measured accuracies are inadequate for fulfilling the AAMI standard. On the other hand, the deflating mode yielded better results for diastolic pressure and the inflating mode for systolic pressure. For the cardiac patient group, the mean error was (-0.6 ± 5.1) mmHg for diastolic and (0.7 ± 6.3) mmHg for systolic pressure, which are considerably better than the accuracies obtained with the healthy group and fulfil AAMI standard. This can be explained by the pulse rising time: the larger the rise time, the larger the pulse delay change, which allows a more accurate determination of diastolic blood pressure.

59 Table 10. Accuracy (average values ± standard deviations) of the diastolic and systolic blood pressure measurements in the healthy and the cardiac patient group using the EP method. The reference value is the average value of IA diastolic, mean and systolic blood pressure. (VIII) Mode

Mean errors Diastolic

Systolic

(mmHg)

(mmHg)

Supine

−5.5 ± 5.2

−1.7 ± 7.9

Sitting

−11.2 ± 6.1

−3.2 ± 6.7

Supine

2.0 ± 6.6

−6.3 ± 9.2

Sitting

0.1 ± 7.3

−7.1 ± 8.3

Supine

−0.6 ± 5.1

0.7 ± 6.3

Healthy Inflating Deflating Patients Inflating

Other possible determinants for systolic, mean and diastolic blood pressure errors were examined by means of correlation calculations. Correlations between pressure errors and IA pressures were fairly low, the only exception being the correlation between IA diastolic pressure and EP diastolic pressure error, which was 0.54. We also noted that the pressure errors were unaffected by the transducer array’s amplitude level. Table 11 presents the above-mentioned indices and the corresponding correlations between the obtained systolic blood pressure errors. The indices are shown in a descending order according to the correlation coefficient. It can be seen that features of the rising edge of the pressure pulse correlate better with the systolic error than those of the falling part. The first derivative peak level of each pulse pressure describes the pulse's highest rise rate. The table also indicates that many features have no correlation with systolic pressure error. If the absolute value of the correlation coefficient is less than 0.3, the correlation is considered negligible. Thus, there seems to be an insignificant correlation between systolic pressure error and factors like pulse wave velocity, oxygen saturation, cardiac output, body mass index, peripheral temperature, diastolic and mean pressure, age and weight.

60 Table 11. Correlations between systolic blood pressure error and different indices of the pressure pulse waveform. All values are average values. (V) Correlation coefficient R First derivative peak level of pulse pressure

-0.52

Rising time (10%…90%)

0.49

Time from 50% of pulse pressure to the maximum

0.46

(MAP-DIAS)/(SYS-DIAS)x100%

0.44

Time from diastolic to systolic pressure (0%…100%)

0.40

Systolic pressure (last palpated pulse)

0.35

Arm circumference

0.32

Augmentation (visual)

0.32

SYS, Systolic pressure (intra-arterial)

-0.31

Pulse width at 80% level of pulse pressure

0.27

Electronically palpated amplitude

0.21

Weight

0.17

Age

0.16

MAP, Mean arterial pressure

-0.14

DIAS, Diastolic pressure (intra-arterial)

-0.13

Peripheral temperature

-0.12

BMI, Body mass index

0.11

Height

0.07

PCWP, Pulmonary capillary wedge pressure

-0.06

CO, Cardiac output

0.05

SpO2, Saturation of oxygen

0.04

PWV, Pulse wave velocity

0.03

Blood temperature

0.00

Linear regression can be used to diminish the dependence of each index. The index having the highest correlation index was the first derivative peak level of the pulse pressure. Without correction, the linear regression equation for the electronic palpation method vs. intra-arterial method is y = 0.79x + 29.05 and the correlation coefficient R2 = 0.61. Fig. 24 displays the first derivative peak level of the pulse pressure method, a corrected scatter gram, the equation and the coefficient, which all show improvement. The palpated signal can be corrected likewise: if the measuring bandwidth is, say 0.2-15 Hz, the palpated signal is not distorted and the waveform closely resembles that of the intra-arterial pressure pulse. Hence, this accuracy enhancement method can be easily adapted to the noninvasive electronic palpation method.

61

Corrected el. palpated systolic blood pressure (mmHg)

200 y = 0.84x + 19.17 R2 = 0.68

150 100 50 0 0

50

100

150

200

Intra-arterial systolic blood pressure (mmHg)

Fig. 24. Corrected systolic blood pressure values obtained by the electronic palpation method as a function of intra-arterial systolic blood pressure. (V)

4.5 Blood pressure determination in the oscillometric method In this method, the cuff pressure signal p(i) is first filtered using two-second moving average filtering, i.e., every sample is averaged 99 samples back and 100 samples forward using the formula: p(i) = mean[p (i-99 : i + 100)],

(4)

where p(i) is the value of the sample and i the corresponding index. The resulting pressure signal ignores one hundred samples from the beginning and another hundred from the end, which has to be considered in later calculations. The filtered signal is then subtracted from the original pressure signal yielding a high-pass filtered oscillation signal with negligible attenuation and phase distortion. This procedure allows the study of the potential effects of analogue filtering on the oscillation curve. The moving average filtered signal serves as a cuff pressure reference signal. To assess the influence of filtering on characteristic ratios, Fig. 25 presents both the analogically filtered (HW filtering) and the amplified oscillation signal as well as the signal gained by subtracting the two-second moving average filtered signal (SW filtering) from the original cuff signal. Cuff pressure was first low-pass filtered (0-14 Hz, fourth order) and then HW band-pass filtered (1-15 Hz for the healthy group, 1.7-11 Hz for the cardiac patient group) and, finally, amplified to an appropriate signal level. The lowest SW-filtered signal was gained by subtracting the two-second moving average low-pass filtered cuff oscillation signal from the original cuff pressure signal. The thus obtained signal is a high-pass filtered signal with a very low cut-off high-pass frequency. The signal is then amplified to an appropriate signal level using a software multiplier. Oscillation amplitudes are determined by subtracting diastolic pressure from systolic pressure (the

62 lower OSC signal). Alternatively, the following lowest value can be subtracted from the highest value (the upper OSC signal) to get an amplitude value with pulsation amplitude levels and the corresponding cuff pressures.

150 Cuff oscillations (a.u.)

IA blood and cuff pressures (mmHg)

Intra-arterial blood pressure Cuff pressure

100 50 HW filtered oscillation 0

SW filtered oscillation

0

5

Amplitude determination

10 Time (s)

15

20

25

Fig. 25. From top to bottom: IA blood pressure, increasing cuff pressure, analogically hardware-filtered (HW) and software-filtered (SW) cuff oscillation signal (with bias) as a function of time. (VIII)

The oscillation amplitude values can be reconstituted by multiplying the amplitude values by a multiplication table in accordance with the formula k(j)=

mean[p p (first:last)] p p (j)

,

(5)

where pp(j) is IA blood pulse pressure corresponding to the oscillation pulse number j. This method allows the reconstruction of the oscillation amplitude signal so that it is not affected by variations in IA pulse pressure; in other words, the pulse pressure amplitude remains constant. The oscillation amplitude signal must then be filtered to eliminate the effects of motion artifacts. Our experiments showed that three-point moving median filtering is adequate for the purpose. Because the cuff pressure curve is not linear in neither the inflating nor the deflating pressure mode, the oscillation signal must be normalized. After normalization, the amplitude signal is multiplied by a constant to attain the maximum value of 100. Also the corresponding pressure signal is multiplied by a scaling multiplier such that the highest point of the oscillation curve, or 95% of the maximum, corresponds to the average mean IA pressure. Also Geddes et al. (1983) used a similar scaling factor.

63 As mentioned earlier, this “highest point” should be on the diastolic side of the oscillation curve (Ursino and Cristalli, 1994). Fig. 26 presents the normalized oscillation curves before and after multiplication and the average diastolic, mean and systolic blood pressure value as well as the value of the scaling multiplier. The normalized amplitude signal is then interpolated such that a single integer pressure value corresponds to the oscillation amplitude value. If straight lines are drawn up from the averaged diastolic and systolic IA pressure values, they cross the normalized interpolated curve at the characteristic diastolic and systolic points (the CR values in Fig. 26). The measured patient was a 66-year-old woman, who was 172 cm tall, weighed 75.2 kg and had an arm circumference of 33 cm. The width of her pressure cuff was 13 cm. First, the average values for the mean intra-arterial systolic and diastolic blood pressure are calculated. Then, the scaling multiplier is obtained by multiplying the cuff pressure signal by the oscillation amplitude, to attain the 95% amplitude level of the average mean blood pressure. These characteristic ratios are the levels at which the multiplied signal obtains the averaged systolic and diastolic blood pressures values. 100 80 Scaling multiplier=0.85

60

Systolic CR=49 40

0

0

20

40

60 80 Cuff pressure (mmHg)

IAsys=93

20

IAmean=65

Diastolic CR=32 IAdias=51

Normalized oscillation (%)

95% level

100

120

Fig. 26. Determination of the scaling multiplier and characteristic ratios (CR values) for diastolic, mean and systolic blood pressure from the normalized cuff oscillation curve in the oscillometric method. (VIII)

Fig. 27 shows a flowchart of all mathematical operations performed on the data to get the systolic, mean and diastolic blood pressure values. First, cuff pressure is sensed by a DCcoupled transducer. The signal is then amplified and band-pass filtered using both HW and SW filtering. Amplitude values are subsequently obtained through specific amplitude determination for both the HW and SW signals. The data values are filtered using threepoint moving median filtering to eliminate casual motion artefacts. Next, by multiplying every single pressure value by a multiplier acquired from the IA blood pressure measurement, the data values are normalized such that 100% marks the highest value. In addition, the data values are filled to match every pressure integer value with a

64 corresponding normalized amplitude value (interpolation). Now, both signals are multiplied by the previously determined scaling multipliers so that the 95% level corresponds to the mean intra-arterial blood pressure value. Finally, the mean diastolic and systolic blood pressure values are determined separately for both the HW and SW filtered signals using the previously determined characteristic ratios. Cuff pressure sensor amplifier Hardware BP filter (1–14 Hz)

Moving average LP filter

Original signal–filtered signal Peak detection

Peak detection

Amplitude calculation

Amplitude calculation

3 points moving median filtering

3 points moving median filtering

Amplitude normalization

Amplitude normalization

Pressure interpolation

Pressure interpolation

Pressure normalization

Pressure normalization

Mean blood pressure determination Diastolic blood pressure determination Systolic blood pressure determination

Fig. 27. Flowchart diagram depicting the oscillometric method. (VIII)

65

4.6 Accuracy of the oscillometric method The oscillation method described earlier was used to determine characteristic ratios separately for the healthy volunteer and the cardiac patient group. For the first group, also the measurement mode (i.e., inflating and deflating cuff pressure) and position (either supine or sitting) were considered. Table 12 tabulates the average values for these factors along with the standard deviations and scaling multipliers for both groups. As the table shows, the scaling multiplier for the healthy group is about one in every group. In addition, there is only a slight deviation between measurement mode and position. The characteristic ratios were found to range from 40% to 48% for diastolic blood pressure and from 49% to 59% for systolic blood pressure in the different modes. It should be noted that the values for the diastolic characteristic ratios differ considerably from the mean values reported by Geddes (82%), Drzewiecki (72%) and Ursino (70%). Geddes conducted his measurements on humans, while Drzewiecki and Ursino also used mathematical models and a simple blood pressure signal mode. One explanation to account for the observed discrepancy between their results and the ones reported here is the different oscillation filtering bandwidth used in our experiments. The characteristic ratios for systolic blood pressure obtained in the present study lay within the same range as those reported in the earlier experiments (55%, 59%, 52%, respectively). Another interesting finding is that the standard deviation of the characteristic ratio for diastolic blood pressure was only about 50% of that calculated for systolic pressure. With the cardiac patient group, the scaling multipliers were less than one, 0.86 or 0.87, depending on the filtering mode. The average values of the characteristic ratios were somewhat lower for systolic blood pressure and the standard deviations slightly higher than the corresponding figures for the healthy group. The oscillation amplitude curve was gained by subtracting the two-second moving average filtered cuff oscillation from the unfiltered oscillation curve (SW filtering), which resulted in a low degree of distortion. As analogically filtered signals (HW filtering) gave almost identical characteristic ratios, filtering did not have a great effect on accuracy. Table 12. Characteristic ratios (mean values ± standard deviations) for diastolic and systolic blood pressure in the healthy and cardiac patient group. (VIII) Mode

Characteristic ratios, %

Scaling multiplier

Diastolic

Systolic

Supine

39.6 ± 7.4

48.5 ± 11.6

0.96 ± 0.05

Sitting

45.7 ± 9.4

55.2 ± 16.7

1.01 ± 0.08

Supine

44.3 ± 6.6

51.2 ± 13.2

1.02 ± 0.05

Sitting

48.1 ± 8.7

58.5 ± 15.3

1.08 ± 0.06

Supine

45.8 ± 15.6

40.6 ± 12.3

0.87 ± 0.07

Supine

43.9 ± 10.4

47.1 ± 13.9

0.86 ± 0.07

Healthy Inflating Deflating Patients Inflating, HW filtering Inflating, SW filtering

66 These average characteristic ratio values were used to determine both systolic and diastolic pressure as well as pressure errors for both groups. The reference values were averaged from the IA pressure waveforms during cuff inflation or deflation, and the mean arterial pressure was determined at the point where the cuff oscillation reached 95% of its maximum amplitude curve. The results are presented in Table 13. The pressure error in the healthy group was found to be fairly low, except in the case of sitting test subjects measured during deflation of the cuff, which is the standard procedure used in clinics. This may be due to hydraulic head, i.e., a difference in blood level between the reference transducer (catheter) and the cuff. For the patient group, however, the average errors were high, producing overestimates in excess of 10 mmHg, which is outside the standard range. It must be pointed out that the width of the cuff used in these measurements was 40% or more of the subjects’ arm circumference, thus fulfilling the requirements of the standards. In both groups, the standard deviation of errors for systolic blood pressure was too high to fit the standards, regardless of cuff pressure mode and posture. On the other hand, in measurements of diastolic and mean artery pressure, these deviations were less than 8 mmHg, which fulfils the requirements. Table 13. Accuracy (mean values ± SD) of the diastolic, mean and systolic blood pressure measurements in the healthy and cardiac patient group using the OSC method. The reference value is the average value of IA diastolic, mean and systolic blood pressure. (VIII) Mode

Posture

Mean errors Diastolic

Mean

Systolic

(mmHg)

(mmHg)

(mmHg)

Healthy Inflating Deflating

Supine

2.8 ± 4.5

4.1 ± 5.0

4.0 ± 8.4

Sitting

−0.3 ± 5.4

0.1 ± 6.9

−1.9 ± 10.1

Supine

−1.1 ± 5.1

−1.4 ± 3.7

−3.3 ± 8.5

Sitting

−4.6 ± 6.3

−6.5 ± 4.5

−9.0 ± 11.0

Supine

12.7 ± 6.1

13.4 ± 6.8

15.0 ± 11.6

Supine

10.0 ± 5.3

12.3 ± 7.0

10.2 ± 12.0

Patients Inflating, HW filtering Inflating, SW filtering

5 Discussion The historical review showed that, for centuries, blood pressure has been of great importance both for human health diagnostics and device development. Yet, almost all clinically approved methods are based on measuring counterpressure and involve the use of a cuff. This is equally true of “the gold standard”, i.e., the auscultatory method, and the newer oscillometric method, as well as of the volume clamp method. Some methods, such as the tonometric and the PTT method (based on pressure pulse transit time), operate on another principle than counterpressure. Nonetheless, they still require a cuff. In addition, to work accurately, tonometers demand an accurate hold-down pressure, which should be controlled periodically. As to the pulse transit-time method, it is also calibrated using a cuff, but, like tonometers, it can measure blood pressure continuously, pulse-bypulse. This characteristic makes it an attractive method for diurnal, 24-hour, recordings. If artefact cancellation is added to the measurement system, even accurate ambulatory recordings can be achieved. According to O’Brien’s review paper, published in 2001, only few commercially available blood pressure measurement devices have passed both the AAMI and the BHS standard, and can thus be recommended. Since then, some of them have passed at least one of the common standards, AAMI, BHS or International Protocol, of which the oldest one, AAMI, requires an accuracy of (5 ± 8) mmHg. None of the recommended noninvasive methods is suitable for continuous measurement. According to Guelen’s results from 2003, FinometerTM fulfilled the requirements after two sequential corrections. However, the conclusion was not supported by Elvan-Taspinar et al. in the same year. This method may be not suitable for long-term measurement, because it can be painful in the long run, and it is certainly unsuitable for ambulatory measurements, owing to high power need and artefact sensitivity. Jagomägi et al.’s modified oscillometric measurement system has the capacity to make continuous measurements with an accuracy equalling that of Finapres, Finometer and Portapres. All these methods are also very sensitive for vasoconstriction in peripheral vessels. One of the most interesting techniques is the pulse transit-time method, which can be used for continuous noninvasive blood pressure measurement. In this method, a cuff is used for calibration and variations in blood pressure are recognized on the basis of rapid changes in pulse transit time. In addition, the application of an adequate motion artifact

68 cancellation method makes it suitable for long-term measurements. Artifact cancellation can be achieved, for example, by using new, low-power multi-axial acceleration MEMS sensors. Recent results by Poon & Zhang in 2005, who used the PPG method to detect blood pressure pulsations in a finger and achieved an accuracy of (0.6 ± 9.8) mmHg and (0.9 ± 5.6) mmHg for systolic and diastolic pressure, respectively, demonstrate that the method has great potential for wearable blood pressure monitoring. We may conclude that since a large majority of recent papers deal with it, the PPG method is the most popular among research groups. Unfortunately, this method requires power to transmit IR or visible light into finger tissue. This requirement can, however, be reduced by decreasing the duty cycle of the transmitted light. In addition, to avoid background noise, the measurement demands high frequency pulses, which usually results in a need to apply either high-order band-pass filtering or synchronous detection. Thus, circuit complexity can be considered to increase, with a resultant increase in power consumption. EMFi material proved applicable to detecting blood pressure pulsations on the radial artery and can also be used for pulse shape and velocity measurements. As shown in Paper VI, the material has the ability to distinctly measure pressure pulsations on subcutaneous arteries; the average correlation coefficient between intra-arterial and noninvasive radial pressure pulsations was 0.98, using identical filter bandwidths. EMFi sensor material needs an operational amplifier operating as a transducer for detection. Power consumption is not a problem, however, since it is possible to use EMFi material in conjunction with an ultra low power operational amplifier, such as the TLC1079 by Texas Instruments, which typically consumes only 10 µW per channel. Disadvantages of EMFi material are sensitivity variations and decreased sensitivity as a function of time. In addition, a total disaster is possible, if a certain temperature, the socalled Curie temperature, is exceeded. EMFi material can be used successfully as a transducer at room temperature, making it a feasible alternative for hospital, point of care and homecare environments. Also, if a modern low-power acceleration MEMS sensor is applied on the sensor array package, dynamic hydraulic pressure noise caused by hand movements and other motion artefacts can be cancelled or considerably reduced. By fulfilling the AAMI standard, the electronic palpation method proved accurate with the elderly cardiac patient group. With the healthy volunteers, however, the method only passed the requirements when it came to deflating cuff pressure and determination of diastolic pressure. The difference of the diastolic errors can be easily explained: the cardiac patient group exhibited a slower pressure pulse slope and therefore a larger delay time change during the measurement, yielding a clear indication of the changing point. In the EP method, when cuff pressure exceeds the venous pressure level, the volume of blood in the hand starts to increase with each heart beat. This induces vascular congestion and a pressure increase in the veins and surrounding tissues on the distal side of the cuff. The velocity of the pressure pulse waves and therefore also the delay change depends exponentially on vascular compliance and the pressure difference between the inner and outer arterial wall. A change in this pressure, also known as transmural pressure, reduces pulse wave velocity and slightly increases pulse delay, before the diastolic blood pressure level is achieved. This phenomenon is illustrated in Fig. 28. Intra-arterial (IA) blood pressure is measured invasively with a catheter tip and radial artery pulsation noninvasively with an electronic palpation transducer. As seen, the recorded venous pressure increases slightly until cuff pressure drops under it. The same

69

100

0

0

-100

-200

TimeTime delay (ms) delay (ms)

-200

50

5

10

5

15

10

15

20 25 Time (s) 20 Time (s)

30 25

35 30

40 35

40

Electronic palpation signal (a.u.)

200 200

Electronic palpation signal

IA & IV pressure (mmHg)

Cuff pressure, intra-arterial and intra-venus pressure (mmHg) Cuff pressure,

phenomenon occurs also with inflating cuff pressure. Owing to venous congestion, the pulse is still slightly delayed at intermediate cuff pressures between the diastolic and intravenous level. The vertical line in the centre represents the average diastolic blood pressure value during the measurement, and the other lines its minimum and maximum values. According to our experiments (Papers VI, VII & VIII), the deflating mode is preferable, due to a smaller increase in vascular pressure. In addition, patients might have had a larger capillary and vein volume on the distal side of the cuff, so the pressure increase in the hand during cuff deflation was smaller than the corresponding value for the healthy volunteers.

50

0 0

-50 0

-50 0

20

20

40

40

60 80 100 Cuff pressure (mmHg) 60 80 Cuff pressure (mmHg)

100

120

140

120

140

Fig. 28. Upper figure: pulsations in right (intra vascular, IV) and the left (noninvasive electronic palpation sensor) radial artery, cuff pressure and intra vascular (IV) venous pressure as a function of time. Lower figure: pulse delay between pulses as a function of cuff pressure. The vertical lines in the lower figure are the minimum, average and maximum values of IA diastolic blood pressure. (VI)

On other hand, the inadequate accuracy observed in systolic blood pressure determination with the healthy group was unexpected, but may be due to imprecise system calibration; a level change of ten centimetres yields an error of nearly 8 mmHg, owing to hydraulic pressure in the artery. The author did not attend the tests with the healthy volunteers. In the EP method, the ECG signal, being easily detectable in normal subjects, can be used as a timing reference. However, since electrical cardiac activation (ECG) does not constitute mechanical activation (Hong 2000), it is advisable to use pressure pulses

70 instead. The R-spike is followed by a pre-ejection period before the aortic valve opens. This time period depends on aortic diastolic pressure and heart constriction rate, and the resulting deviation in pressure pulse delay renders the determination of blood pressure inaccurate (Meigas et al. 2001). The optimal reference point is the radial artery of the other hand, which usually produces an almost identical pulse waveform, thereby greatly facilitating the accurate detection of pulse delays. This method does not provide any information on pulse wave velocity, which could be used for continuous, pulse-to-pulse monitoring of blood pressure (Chen et al. 2000, Meigas et al. 2001 and 2003). Nevertheless, pulse wave velocity can be clearly specified in measurements made on one hand using, for example, the brachial and radial arteries. The shape of pressure pulses, especially optically measured plethysmographic pulses, is quite smooth, and does not provide a very distinct timing point. A better timing reference may be obtained with optical interferometers, such as laser-Doppler systems based on the self-mixing effect (Hast et al. 2001-2003, Hast 2003, paper IX and Meigas et al. 2001 and 2003). These interferometers measure skin vibrations over an artery with a very high degree of accuracy, restricted theoretically only by the wavelength of the laser divided by two (~300 - 700 nm). Their main advantage is the absence of a loading effect, as the sensor is not in contact with the skin. Also, unlike tonometer type pressure transducers (Drzewiecki et al. 1983 and 1996, Kelly et al. 1989, Kemmotsu et al. 1991, Bansal et al. 1994, Drzewiecki and Pilla 1998, Fetics et al. 1999), laser-Doppler interferometers do not suffer from sensor movements caused by arterial pulsations. Further, their timing accuracy is very good, because the signal has a much wider bandwidth than a pressure signal. In principle, the time delay method can be used with any transducer that measures pulsations either on the radial artery or a finger. One possibility is to measure noninvasively radial artery pressure pulses in both hands using either two identical pressure array transducers or interferometers with the same bandwidth. In addition, by measuring the starting time of pressure pulses (opening of the aortic valve) using, for example, a contact microphone on the chest, we will be able to determine the pulse waveform and pulse wave velocity (PWV), which enables noninvasive, pulse-by-pulse monitoring of blood pressure. This requires a daily calibration measurement using an arm cuff. Also pressure pulses measured in the brachial artery under the cuff can be used as a timing base. Finally, detecting the waveform, amplitude and time of pulsations in the radial artery can be utilized in many ways: for example, with other noninvasively measured physiological signals, they can be used to determine arterial compliance or stiffness (O’Rourke et al. 1992, Asmar et al. 1995, Safar et al. 1998, Blacher et al. 1999, O’Rourke and Mancia 1999 and Asmar 1999) and central aortic pressure (Kelly et al. 1989, Karamonoglu et al. 1993, Chen et al. 1997 and Fetics et al. 1999). The mean errors obtained with the OSC method in the cardiac patient group were too large to meet the AAMI and BHS standards, which call for an inaccuracy level of less than 5 mmHg and a standard deviation of less than 8 mmHg. Our analysis employed a scaling multiplier to get the 95% level of the oscillation curve to cross at the mean intraarterial pressure point. Without a scaling multiplier, the maximum oscillation in Fig. 26 is about 78 mmHg, which overestimates mean blood pressure by 13 mmHg. The corresponding figures for the EP method in the same measurement are 56/94 mmHg for diastolic and systolic pressure, while intra-arterial diastolic and systolic pressures

71 alternate between 47 and 54 mmHg and 89 and 99 mmHg, respectively. This shows that the point where the change of delay appears gives a reasonably good estimation of diastolic blood pressure. In the OSC method, however, maximum oscillation amplitude is attained at a pressure higher than mean intra-arterial pressure, producing false readings.

6 Conclusion This study first described noninvasive blood pressure measurements in detail, starting from the historical background and extending to modern methods. The next stage involved designing, building and testing EMFi-based sensor arrays, which were subsequently found to be adequate for monitoring blood pressure pulsations in the radial artery. Their material properties were appropriate for long-term low-power applications based on sensing force/pressure pulsations at near room temperature. Nonetheless, the material’s unhomogeneity still obstructs their application to blood pressure measurement systems of the tonometer type. At the following stage, the developed measurement system was used at the Oulu University Hospital for two sets of measurements, conducted in 1997 – 1999. In the second set, the measurement system consisted of a microcontroller-based test device for determining blood pressure values and a data recording measurement system for further analysis. The test device worked as intended. The determination of diastolic blood pressure was described, and the available methods’ accuracy was studied. Using intra-arterial blood pressure as a reference, this study investigated two measuring methods, the oscillometric (OSC) and the electronic palpation (EP) method. The test subjects comprised a group of healthy volunteers and a group of cardiac patients, who had undergone either a bypass or a valve operation, or both. The healthy group was measured twice using both inflating and deflating cuff pressure in a sitting and supine position with a one-minute follow-up period between the measurements. The cardiac patient group, on the other hand, was measured only in the supine position with increasing cuff pressure. All measurements were analyzed with the aim of ensuring the best possible accuracy level for both groups. Thus, in the OSC method, case-specific characteristic ratios were used to reach an ideal degree of accuracy. In the EP method, the determination of systolic blood pressure was based on the last palpated pulse, while the determination of diastolic pressure relied on changes in the time delay of the detected signals produced by the occlusion cuff. For better timing accuracy, interpolation was used to increase the sampling rate by a factor of ten. In the oscillometric method, the acquired characteristic ratios for the determination of diastolic blood pressure differed considerably from those reported in the literature, whereas the characteristic ratios for systolic blood pressure were within the reported

73 ranges. It was also established that cuff pressure mode affects accuracy: in the deflating mode, the characteristic ratios were slightly higher than in the inflating mode. Also posture had an effect: both characteristic ratios increased by about 4-8 percent when the test subjects were sitting. Age, on the other hand, seemed to have only a minute effect on these characteristic ratios, provided that the same cuff pressure mode and position were used in all measurements. In the healthy group, the EP method was found to provide a slightly better degree of accuracy with deflating cuff pressure. With a mean error of about zero and a standard deviation of about 5-6 mmHg, also the inflating mode used in the cardiac patient group gave excellent results. Nevertheless, to avoid vascular swelling, the deflating pressure is the more recommendable of the two. To sum up, a comparison between the OSC and the EP method indicates that the EP method offers a feasible alternative to the OSC method, particularly because it also produces information on the delay and waveform of pressure pulses. As a consequence, it can be applied to continuous blood pressure monitoring and the noninvasive determination of such physiological indices as arterial compliance and central aortic pressure.

References Alametsä J, Koivuluoma M & Varri A (2001) Mobile physiological signal measurement station. Proc. International Federation for Medical and Biological Engineering, 9th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON, Pula, Croatia 1: 289–292. Alametsä J, Värri A, Koivuluoma M & Barna L (2004) The potential of EMFi sensors in heart activity monitoring. 2nd OpenECG Workshop Integration of the ECG into the HER & interoperability of ECG device systems, Berlin, Germany. Junnila S Akhbardeh A, Varri A & Koivistoinen T (2005) An EMFi-film sensor based ballistocardiographic chair: performance and cycle extraction method, Signal Processing Systems Design and Implementation, IEEE Workshop on, Athens, Greece 1: 373–377. Akhbardeh A Junnila S Koivistoinen T Koobi T & Varri A (2005) Ballistocardiogram classification using a novel transform so-called AliMap and biorthogonal wavelets. Intelligent Signal Processing, IEEE International Workshop 1: 64–69. Koivistoinen T, Junnila S, Varri A & Koobi T (2004) A new method for measuring the ballistocardiogram using EMFi sensors in a normal chair. EMBC Proc. 26th Annual International Conference 1(3): 2026–2029. Anttonen J & Surakka V (2005) Emotions and heart rate while sitting on a chair. CHI2005, Portland, Oregon, USA: 491–499. Alexander H, Cohen M & Steinfeld L (1977) Criteria in the choice of an occluding cuff for the indirect measurement of the blood pressure. Med Biol Eng Comput 15: 2–10. Allen J, Gehrke T, O’Sullivan JJ, King ST & Murray A (2004) Characterization of the Korotkoff sounds using joint time-frequency analysis. Physiol Meas 25: 107–117. Antila M, Muurinen T, Linjama J & Nykänen H (1997) Measurement methods of flat panel electromechanical film loudspeakers. Proc. Active 97, Budabest, Hungary 1: 607–618. Antila M, Saarinen K & Kirjavainen K (1998) Performance of a large, flat electromechanical film actuator panel. Proc. Actuator 98, 6th International Conference on New Actuators 1: 96–99. Asada H, Reisner A, Shaltis P & McCombie D (2005) Towards the Development of Wearable Blood Pressure Sensors: A Photo-Plethysmograph Approach Using Conducting Polymer Actuators. IEEE-EMBS, Proc. 27th Annual International Conference, Shanghai, China: 4156– 4159. Asada HH, Shaltis P, Reisner A Rhee S & Hutchinson RC (2003) Mobile monitoring with wearable photoplethysmographic biosensors. IEEE Eng Med and Biol Magazine 22(3): 28–40. Asmar R (1999) Arterial Stiffness and Pulse Wave Velocity, Clinical Applications. Éditions scientifiques et médicales Elsevier SAS. 23, rue Linois, 75724 Paris cedex 15.

75 Asmar R, Benetos A, Topouchian J, Laurent P, Pannier B, Brisac A-M, Target R & Levy BI (1995) Assessment of arterial distensibility by automatic pulse wave velocity measurement. Hypertension 26(3): 485–490. Association for the Advancement of Medical Instrumentation. American national standard. Electronic or automated sphygmomanometers. ANSI/AAMI SP 10–1987. Arlington, VA: AAMI, 1987: 25. Association for the Advancement of Medical Instrumentation. American national standard. Electronic or automated sphygmomanometers. ANSI/AAMI SP 10–1992. Arlington, VA: AAMI, 1993: 1–40. Backman J & Karjalainen M (1990) Audio and ultrasonic transducers based on electrothermomechanical film (ETMF). Proc. IEEE Int. Conf. on acoustics, speech and signal processing, Albuquerque, NM, USA 2: 1173–1176. Backman J (1990) Audio applications of electrothermomechanical film (ETMF). J Aud Eng Soc 38: 364–371. Bansal V, Drzewiecki G & Butterfield R (1994) Design of a flexible diaphragm tonometer, Proc. 13th S Bioeng Conf, Washington, DC, USA: 148–151. Birch AA & Morris SL (2003) Do the FinapresTM and Colin® radial artery tonometer measure the same blood pressure changes following deflation of thigh cuffs? Physiol Meas 24(3): 653–660. Blacher J, Asmar R, Djane S, London GM & Safar ME (1999) Aortic pulse wave velocity as a marker of cardiovascular risk in hypertensive patients. Hypertension 33 (5): 1111–1117. Boehmer RD (1987) Continuous, real-time, noninvasive monitor of blood pressure: Penaz methodology applied to the finger. J Clin Monit 3(4): 282–287. Bramwell JC & Hill AV (1922) The velocity of the pulse wave in man. Proc. Royal Soc Biom, 96: 298–306. Bur A, Hirschl MM, Herkner H, Oschatz E, Kofler J, Woisetschlager C & Laggner AN (2000) Accuracy of oscillometric blood pressure measurement according to the relation between cuff size and upper-arm circumference in critically ill patients. Crit Care Med 28(2): 371–376. Callaghan FJ, Geddes LA, Babbs CF & Bourland JD (1986) Relationship between pulse-wave velocity and arterial elasticity. Med Biol Eng Comput: 248–254. Chan KW, Hung K & Zhang YT (2001) Noninvasive and cuffless measurements of blood pressure for telemedicine. IEEE-EMBS, Proc. 23rd Annual International Conference, Istanbul, Turkey (4): 3592–3593. Chen C-H, Nevo E, Fetics B, Pak PH, Yin FC, Maughan WL & Kass DA (1997) Estimation of central aortic pressure waveform by mathematical transformation of radial tonometry pressure; validation of generalized transfer function. Circulation 95(7): 1827–1836. Chen W, Kobayashi T, Ichikawa S, Takeuchi Y & Togawa T (2000) Continuous estimation of systolic blood pressure using the pulse arrival time and intermittent calibration. Med Biol Eng Comput 38(5): 569–574. Ciaccio E & Drzewiecki G (1988) Array sensor for arterial pulse recording-reduction of motion artifact. Bioengineering Conference, Proc. 14th Annual Northeast, Durham, NH, USA: 66–69. Ciaccio E, Drzewiecki G & Karam E (1989) Algorithm for reduction of mechanical noise in arterial pulse recording with tonometry. Bioengineering Conference, Proc. 15th Annual Northeast: 161– 162. Colak S & Isik C (2003) Fuzzy oscillometric blood pressure classification. Fuzzy Information Processing Society, NAFIPS, 22nd International Conference of the North American, USA: 208– 213. Colak S & Isik C (2003) Systolic blood pressure classification. Neural Networks, Proc. International Joint Conference on 1: 627–630. Colak S & Isik C (2004) Blood pressure estimation using neural networks. Proc. IEEE International Conference on Computational Intelligence for Measurement Systems and Applications: 21–25.

76 Colak S & Isik C (2004) Evaluation of pulse features for oscillometry. Proc. IEEE 30th Annual Northeast Bioengineering Conference: 93–94. Colak S & Isik C (2004) Simulation of oscillometric waveforms. Canadian Conference on Electrical and Computer Engineering: 497–500. Cozby RR & Adhami RR (1993) Low-Frequency Korotkoff signal analysis and application. IEEE Trans Biomed Eng 40(10): 1067–1070. Cristalli C, Neuman MR & Ursino M (1994) Studies on soft tissue pressure distribution in the arm during non-invasive blood pressure measurement. Engineering Advances: New Opportunities for Biomedical Engineers, Proc. 16th Annual International Conference of the IEEE-EMBS 1: 41–42. Cristalli C, Mancini P & Ursino M (1992) An experimental and mathematical study of noninvasive blood pressure measurement. Proc. Annual International Conference of the IEEE-EMBS 14(1): 63–64. Cristalli C, Ursino M, Palagi F & Bedini R (1993) FEM simulation and experimental evaluation of the 'squeezing' phenomenon in Riva-Rocci blood pressure measurement. Proc. Comput Cardiology, London, Great Britain: 655–658. Drzewiecki G & Pilla JJ (1998) Noninvasive measurement of the human brachial artery pressurearea relation in collapse and in hypertension. Ann Biomed Eng 26: 967–974. Drzewiecki G (1995) Noninvasive assessment of arterial blood pressure and mechanics. In: Bronzino JD (ed) The Biomedical Engineering Handbook. Trinity College, Hartford, Connecticut, USA: 1198. Drzewiecki G, Bansal V, Karam E, Hood R & Apple A (1993) Mechanics of the occlusive arm cuff and its application as a volume sensor. IEEE Trans Biomed Eng 40: 704–708. Drzewiecki G, Hood R & Apple A (1994) Theory of the oscillometric maximum and the systolic and diastolic detection ratios. Ann Biomed Eng 22: 88–96. Drzewiecki G, J Melbin & Noordengraaf A (1983) Arterial tonometry: review and analysis. J Biomech 16: 141–152. Drzewiecki G, Solanki B, Wang J-J & Li JK-L (1996) Noninvasive determination of arterial pressure and volume using tonometry. ELECTRO '96. Professional Program. Proc 1: 61–63. Epstein RH, Huffnagle S & Bartkowski RR (1991) Comparative accuracies of a finger blood pressure monitor and an oscillometric blood pressure monitor. J Clin Monit 7(2): 161–167. Epstein RH, Kaplan S, Leighton BL, Norris MC & DeSimone CA (1989) Evaluation of a continuous noninvasive blood pressure monitor in obstetric patients undergoing spinal anesthesia. J Clin Monit 5(3): 157–163. Erlanger JA (1904) A new instrument for determining the minimum and maximum blood-pressure in man. Johns Hopkings Hosp Rep 12: 53–110. Fetics B, Nevo E, Chen C-H & Kass D (1999) Parametric model derivation of transfer function for noninvasive estimation of aortic pressure by radial tonometry. IEEE Trans Biomed Eng 46(6): 698–706. Ficher M, Kirjavainen K, Vainikainen P & Nyfors E (1990) Sensor for the measurement of pressure. Proc. 20th European microwave conference, Budapest, Hungary 1: 985–989. Franchi D, Bedini R, Manfredini F, Berti S, Palagi G., Ghione S & Ripoli A (1996) Blood pressure evaluation based on arterial pulse wave velocity. Computers in Cardiology 1: 397–400. Friedman DB, Jensen FB, Matzen S & Secher NH (1990) Non-invasive blood pressure monitoring during head-up tilt using the Penaz principle. Acta Anaesthesiol Scand 34(7): 519–522. Friesen RH & Lichter IL (1981) Indirect measurement of blood pressure in neonates and infants utilizing an automatic noninvasive oscillometric monitor. Anesth Analg 10: 742–745. Geddes LA & Whistler SJ (1978) The error in indirect blood pressure measurement with incorrect size of cuff. Am Heart J 96: 4.

77 Geddes LA, Boelz MH, Babbs CF, Bourland JD & Tacker WA (1981) Pulse transit time as a indicator of arterial blood pressure. Psychophysiology 18: 71–74. Geddes LA, Voelz M, James S & Reiner D (1981) Pulse wave velocity as a method of obtaining systolic and diastolic blood pressure. Med Biol Eng Comput 19: 671–672. Geddes LA, Voelz M & Combs C (1983) Characterization of the oscillometric method for measuring indirect blood pressure. Ann Biomed Eng 10: 271–280. Geddes LA (1991) Handbook of Blood Pressure Measurement. Humana Press. Gerhard-Multhaupt R (2002) Less can be more: Holes in polymers lead to a new paradigm of piezoelectric materials for electret transducers. IEEE Trans Dielectics and Electrical Insulation 9: 850–859. Gerhard-Multhaupt R (2002) Voided polymer electrets –New materials, new challenges, new changes. Proc. 11th International Symposium on Electrets: 36–45. Gerhard-Multhaupt R, Wegener M, Wirges W, Giacometti JA, Altafilm RAC, Santos LF, Faria RM & Paajanen M (2002) Electrode poling of cellular polypropylene films with short high-voltage pulses. Proc. Annual Report Conference on Electrical Insulation and Dielectric Phenomena: 299–302. Giller CA & Aaslid R (1994) Estimates of pulse wave velocity and measurement of pulse transit time in the human cerebral circulation. Ultrasound Med Biol 20(2): 101–105. Gravenstein JS, Paulus DA, Feldman J & McLaughlin G (1985) Tissue hypoxia distal to a Peñaz finger blood pressure cuff. J Clin Monit 1(2): 120–5. Hämäläinen MK, Parviainen JK & Jääskeläinen T (1996) A novel micromovement actuator manufactured using plastic electromechanical film. Review of Scientific Instruments 67: 1598– 1601. Hasegawa H, Ozawa M, Kanai H, Hoshimiya N & Chubashi N (1997) Evaluation of the elastic property of the arterial wall by measuring small velocity signals using ultrasound. IEEE Ultrasonics Symposium: 1169–1172. Hast J T (2003) Self-Mixing Interferometry and Its Applications in Noninvasive Pulse Detection. Oulu, Finland. Doctoral thesis, University of Oulu, Department of Electrical Engineering. Hast J, Myllylä R, Sorvoja H & Miettinen J (2001) A noninvasive laser Doppler system to study cardiovascular pulsation and its properties. Asian J Phys 10(4): 15 p. Hast J, Myllylä R, Sorvoja H & Miettinen J (2001) Self-mixing interferometry in noninvasive pulse wave velocity measurement. Molecular and Quantum Acoustics 22: 95–106. Hast J, Myllylä R, Sorvoja H & Miettinen J (2002) Comparison between elasticity of the arterial wall and pulse wave velocity with laser Doppler method. Proc SPIE 4619: 259–268. Hast J, Myllylä R, Sorvoja H & Miettinen J (2003) Arterial pulse shape measurement using selfmixing interferometry. Proc. International Society for Optical Engineering (SPIE), Coherence domain optical methods in biomedical science and clinical applications VII, San Jose, USA, 4956: 341–347. Heikkinen LM, Panula HE, Lyyra T, Olkkonen H, Kiviranta I, Nevalainen T & Helminen HJ (1997) Electromechanical film sensor device for dynamic force recordings from canine limbs. Scan J Lab Animal Science 24: 85–92. Hong H (1994) Optical Interferometric Measurement of Skin Vibration for the Diagnosis of Cardiovascular Diseases. Michican: UMI Dissertation Services, A Bell & Howell Company: 111–113. Hori C, Itakura K, Nogawa M, Shirakabe M, Kubota I, Tomoike H & Takatani S (1997) Estimation of aortic BP waveform from noninvasive radial tonometry; validation of FFT and ARX methods. Proc. 19th Annual International Conference of the IEEE-EMBS 3: 1142–1145. Howell WH & Brush CE (1901) A critical note upon clinical methods of measuring blood pressure. Boston Med Surg J 145: 146–151.

78 Hung K, Zhang YT & Tai B (2004) Wearable medical devices for tele-home healthcare. Proc. 26th Annual International Conference of the IEEE-EMBS, San Francisco, USA 2: 5384–5387. Imholz BPM, Wieling W, van Montfrans GA & Wesseling KH (1998) Fifteen years experience with finger arterial pressure monitoring: assessment of the technology. Cardiovascular Research 38(3): 605–616. Jagomägi K, Raamat R & Talts J (2001) Effect of altering vasoactivity on the measurement of finger blood pressure. Blood Press Monitor 6(1): 33–40. Jagomägi K, Talts J, Raamat R & Lansimies E (1996) Continuous non-invasive measurement of mean blood pressure in fingers by volume-clamp and differential oscillometric method. Clin Physiol 16(5): 551–560. Kanai H, Kawabe K, Takano M, Murata R, Chubashi N & Koiwa Y (1994) New method for evaluating local pulse wave velocity by measuring vibrations on arterial wall. Electronics letters 30(7): 534–536. Kanai H, Satoh H, Hirose K & Chubashi N (1993) A new method for measuring small local vibrations in the heart using ultrasound. IEEE Trans Biomed Eng 40(12): 1233–1242. Karamanoglu M, O’Rourke MF, Avolio AP & Kelly RP (1993) An analysis of the relationship between central aortic and peripheral upper limb pressure waves in man. E Heart J 14: 160–167. Kawabe K, Kanai H & Chubashi N (1993) Accuracy evaluation in ultrasonic-Doppler-based measurement of small vibrations for acoustical diagnosis of the aortic wall. Electronics Letters 29(10): 915–916. Kelly R, Hayward C, Ganis J, Daley J, Avolio A & O’Rourke M (1989) Noninvasive registration of the arterial pressure pulse waveform using high-fidelity applanation tonometry. J Vasc Med Biol 1: 142–149. Kemmotsu O, Ueda M, Otsuka H, Yamamura T, Winter DC & Eckerle JS (1991) Arterial tonometry for non-invasive, continuous blood pressure monitoring during anaesthesia. Anaesthesiology 75(2): 333–340. Kerola J, Kontra V & Sepponen R (1997) Non-invasive blood pressure data acquisition employing pulse transit time detection. Proc. 18th Annual International Conference of the IEEE-EMBS, Amsterdam, Netherlands: 1308–1309. Kimble KJ, Darnall RA & Yelderman M et al. (1981) An automatic oscillometric technique for estimating mean arterial pressure in clinically ill neonates. Anesthesiology 54(5): 423–5. Kirjavainen K (1987) Electromechanical film and procedure for manufacturing same. U.S. Patent 4,654,546. Korteweg DJ (1878) Uber die Fortpflanzungsgeshwindkeit des Schalles in elastichen Röhren. Ann Phys Chem 5: 525–542. Kugler J, Rollnik J & Schmitz N (1997) Retest-reliability and convergent validity of noninvasive blood pressure determination: arm sphygmomanometry vs. Penaz-method. Int J Clin Monit Comput 14(4): 251–254. Kurki T, Smith NT, Head N, Dec-Silver H & Quinn A (1987) Noninvasive continuous blood pressure measurement from the finger: optimal measurement conditions and factors affecting reliability. J Clin Monit 3(1): 6–13. Kurki TS, Smith NT, Sanford TJ & Head N (1989) Pulse oximetry and finger blood pressure measurement during open-heart surgery. J Clin Monit 5(4): 221–228. Langewouters GJ, Settels JJ, Roelandt R & Wesseling H (1998) Why use Finapres or Portapres rather than intra-arterial or intermittent non-invasive techniques of blood pressure measurement? J Med Eng Technol 22(1): 37–43. Lee CM & Zhang YT (2003) Cuffless and noninvasive estimation of blood pressure based on a wavelet transform approach. Biomedical Engineering, IEEE-EMBS Asian-Pacific Conference: 148–149.

79 Lee CM & Zhang YT (2003) Reduction of motion artifacts from photoplethysmographic recordings using a wavelet denoising approach. Biomedical Engineering, IEEE-EMBS Asian-Pacific Conference: 194–195. Lekkala J & Paajanen M (1999) EMFi–new electret material for sensors and actuators. Proc. 10th International Symposium of Electrets: 743–746. Lekkala J (1997) The sound of silence… and other applications of the electromechanical film technology. Industrial Horizons: 12–13. Lekkala J, Poramo R, Nyholm K & Kaikkonen T (1996) EMF force sensor–a flexible and sensitive electret film for physiological applications. Med Biol Eng Comput 34(1): 67–68. Lin C-T, Liu S-H, Wang J-J & Wen Z-W (2003) Reduction of interference in oscillometric arterial blood pressure measurement using fuzzy logic. IEEE Trans Biomed Eng 50(4): 432–441. Lindener M, Bauer-Gogonea S, Bauer S, Paajanen M & J Raukola J (2002) Dielectric barrier microdischarges: Mechanisms for the charging of cellular piezoelectric polymers. J Appl Phys 91(8): 5283–5287. Liu S-H & Lin C-T (2001) A model-based fuzzy logic controller with Kalman filtering for tracking mean arterial pressure. IEEE Trans Systems Man Cyber Part A 31(6): 676–686. Maestri R, Pinna GD, Robbi E, Capomolla S & La Rovere MT (2005) Noninvasive measurement of blood pressure variability: accuracy of the Finometer monitor and comparison with the Finapres device. Physiol Meas 26: 1125–1136. Maguire M, Ward T, Markham C, O’Shea D & Kevin L (2001) A comparative study in the use of brachial photoplethysmography and the QRS complex as timing reference in determination of pulse transit time. Proc. 23rd Annual International Conference of the IEEE-EMBS, Istanbul, Turkey: 215–218. Marey EJ (1876) Physiology Experimentale. Paris, Masson & Cie. McDonald DA (1974) Blood flow in arteries. London, Eward Arnold, 2nd edition, Reading 253– 254. Meigas K, Hinrikus H, Kattai R & Lass J (2003) Self-mixing in a diode laser as a method for cardiovascular diagnostics. J Biomed Optics 8(1): 152–160. Meigas K, Kattai R & Lass J (2001) Continuous blood pressure monitoring using pulse wave delay. Proc. 23rd Annual International Conference of the IEEE-EMBS 4: 3171–3174. Moens AI (1878) Die Pulscurve. E J Brill, Leiden 1: 87–95. Mohiaddin RH, Firmin DN & Longmore DB (1993) Age-related changes of human aortic flow wave velocity measured noninvasively by magnetic resonance imaging. J Appl Phys 74: 492– 497. Molhoek GP, Wesseling KH, Settels JJ, van Vollenhoven E, Weeda HW, de Wit B & Arntzenius AC (1984) Evaluation of the Penaz servo-plethysmo-manometer for the continuous, noninvasive measurement of finger blood pressure. Basic Res Cardiol 79: 598–609. Moraes JCTB & Cerulli M (1999) Development of a new oscillometric blood pressure measurement system. Proc. Comp in Card, 1: 467–470. Moraes JCTB & Cerulli M (2000) A strategy for determination of systolic, mean and diastolic blood pressures from oscillometric pulse profiles. Proc Com in Cardiol 1: 211–214. Narimatsu K & Ohmori K (1992) An algorithm for tonometric absolute blood pressure measurement. Proc. Computers in Cardiology, Durham, NC, USA: 559–562. Nelesen RA & Dimsdale JE (2002) Use of radial arterial tonometric continuous blood pressure measurement in cardiovascular reactivity studies. Blood Press Monitor 7(5): 259–263. Neugschwandtner G, Schwödiauer R, Bauer-Gogonea S, Bauer S, Paajanen M & Lekkala J (2001) Piezo-and pyroelectricity of a polymer-foam space-charge electret. J Appl Phys 89(8): 4503– 4511. Nissilä S & Miettinen J (2000) Measuring arrangement. European patent application EP 1 034 736 A1.

80 Novak V, Novak P & Schondorf R (1994) Accuracy of beat-to-beat noninvasive measurement of finger arterial pressure using the Finapres: A spectral analysis approach. J Clin Monit 10(2): 118–126. Nykänen H, Antila M, Kataja J, Lekkala J & Uosukainen S (1999) Active control of sound based on utilizing EMFi technology. Proc. Active 99, Fort Lauderdale: 1159–1170. O’Brien E, Petrie J, Littler W et al. (1990) The British Hypertension Society protocol for the evaluation of automated and semi-automated blood pressure measuring device with special reference to ambulatory systems. J Hypertension 8: 607–619. O’Brien, E, Petrie J, Littler W et al. (1993) Short report: an outline of the revised British Hypertension Society protocol for the evaluation blood pressure measuring devices. J Hypertension 11: 677–679. O'Brien E, Petrie J, Littler W, de Swiet M, Padfield PL, Altman DG, Bland M, Coats A, Atkins N (1993) An outline of the revised British Hypertension Society protocol for the evaluation of blood pressure measuring devices. J Hypertens 11: 677–679. O’Brien E, Waeber B, Parati G, Staessen J & Myers MG (2001) Clinical Review: Blood pressure measuring devices: recommendations of the European Society of Hypertension. BMJ 322: 531– 536. O'Brien E, Pickering T, Asmar R, Myers M, Parati G, Staessen J, Mengden T, Imai Y, Waeber B, Palatini P with the statistical assistance of Atkins N & Gerin W on behalf of the Working Group on Blood Pressure Monitoring of the European Society of Hypertension. (2002) International protocol for validation of blood pressure measuring devices in adults. Blood Press Monitor 7: 3– 17. O’Brien E, Asmar R, Beilin L, Imai Y, Mancia G, Mengden T, Myers M, Padfield P, Palatini P, Parati G, Pickering T, Redon J, Staessen J, Stergiou G, Verdecchia P on behalf of the European Society of Hypertension Working Group on Blood Pressure Monitoring. (2005) Practice guidelines of the European Society of Hypertension for clinic, ambulatory and self blood pressure measurement. J Hypertension 23: 697–701. O’Rourke M & Mancia G (1999) Arterial Stiffness. J Hypertension. 17(1): 1–4. O’Rourke M, Kelly R & Avolio A (1992) The Arterial Pulse. 200 Chester Field Parkway, Malvern, Pennsylvania 19355–9725, USA., Lea & Febiger. Okada M (1988) Possible determinants of pulse-wave velocity in vivo. IEEE Trans Biomed Eng 35(5): 357–361. Paajanen M (2001) The cellular polypropylene electret material-Electromechanical properties. D. Tech. Doctoral Thesis, Department of Electrical Engineering, Tampere University of Technology, VTT Publications, Technical Research Center of Finland, Espoo, Vol. 436. Paajanen M, Lekkala J & Kirjavainen K (2000) Electromechanical film (EMFi) –a new multipurpose electret material. Sensors and Actuators 84: 95–102. Paajanen M, Lekkala J & Välimäki H (2001) Electromechanical modeling and properties of the electret film EMFI. IEEE Trans Dielectrics and Electrical Insulation 8(4): 629–636. Paajanen M, Minkkinen H & Raukola J (2002) Gas diffusion expansion –Increased thickness and enhanced electromechanical response of cellular polymer electret films. Proc. 11th International Symposium on Electrets: 191–194. Paajanen M, Välimäki H & Lekkala J (1999) Modelling the sensor and actuator operations of the electromechanical film EMFi. Proc. 10th International Symposium of Electrets: 735–738. Paajanen M, Välimäki H & Lekkala J (2000) Modelling the electromechanical film (EMFi). J Electrostatics 48: 193–204. Paajanen M, Wegener M & Gerhard-Multhaupt R (2001) Charging of the cellular space-charge electret films in various gas atmospheres. Proc. Annual Report Conference on Electrical Insulation and Dielectric Phenomena: 24–27.

81 Paajanen M, Wegener M & R Gerhard-Multhaupt (2001) Understanding the role of the gas in the voids during corona charging of cellular electret films –a way to enhance their piezoelectricity. J Phys D: Appl Phys 34: 2482–2488. Pachon V (1909) Oscillometre sphygmomanometrique a grande sensibilite constante. Comptes Rendus Soc Biol 66: 776–779. Peltonen J, Paajanen M & Lekkala J (2000) Determination of the actuator sensitivity of electromechanical polypropylene films by atomic force. J Appl Phys 88(8): 4789–4793. Penaz J (1973) Photoelectric measurement of blood pressure, volume and flow in the finger. Proc. Digest of the 10th International Conf. EMBS, Dresden, Germany: 104. Penaz J (1992) Criteria for set point estimation in the volume clamp method of blood pressure measurement. Physiol Res 41(1): 5–10. Penzel T, Brandenburg U, Peter J H & Stamnitz A (1993) Portable continuous non-invasive blood pressure recording for sleep studies. Proc. 15th Annual International Conference of the IEEEEMBS, San Diego, CA, USA: 393–394. Pirttikangas S, Suutala J Riekki J & Röning J (2004) Learning vector quantization in footstep identification. Proc. Pervasive 2004, Second International Conference on Pervasive Computing. Poon CCY & Zhang YT (2005) Cuff-less and Noninvasive Measurements of Arterial Blood Pressure by Pulse Transit Time IEEE-EMBS, Proc. 27th Annual International Conference, Shanghai, China 1: 5877–5880. Posey JA, Geddes LA, Williams H & Moore AG (1969) The meaning of the point of maximum oscillation in cuff pressure in the indirect measurement of blood pressure. Part 1. Cardiovasc Res Cent Bull 8(1): 15–25. Pressman G & Newgard P (1963) A transducer for continuous external measurement of arterial blood pressure. IEEE Trans Biomed Electonics 10: 73–81. Raamat R, Jagomägi K & Talts J (2000) Different responses of Finapres and the oscillometric finger blood pressure monitor during intensive vasomotion. J Med Eng Technol 24(3): 95–101. Raamat R, Jagomägi K, Talts J, Lansimies E, Jurvelin J & Kolari P (2001) Continuous mean arterial pressure measurement in the fingers: the influence of local arm cooling. Med Biol Eng Comput 39(5): 584–589. Raamat R, Jagomägi K, Talts J, Toska K & Walloe L (2001) Beat-to-beat measurement of the finger arterial pressure pulse shape index at rest and during exercise. Clin Physiol Funct Imaging 23(2): 87–91. Raamat R, Jagomägi K, Talts J, Toska K & Walloe L (2001) Recording of short-term finger blood pressure changes induced by an arterial occlusive thigh cuff: comparison between the modified oscillometric and Finapres techniques. Physiol Meas 22(2): N13–20. Raamat R, Talts J, Jagomägi K & Lansimies E (1999) Mathematical modelling of non-invasive oscillometric finger mean blood pressure measurement by maximum oscillation criterion. Med Biol Eng Comput 37(6): 784–788. Räisänen L, Pohjavirta R, Unkila M & Tuomisto J (1992) A new method for the measurement of spontaneous motor activity of laboratory animals. Pharmacol Toxicol 70: 230–231. Ramsey M (1979) Noninvasive automatic determination of mean arterial pressure. Med Biol Eng Comput 17: 11–18. Raukola J, Kuusinen N & Paajanen M (2002) Cellular electets-From polymer granules to electromechanically active films. Proc. 11th International Symposium on Electrets, pp. 195–198. Reeben V & Epler M (1973) Non-invasive continuous measurement of mean arterial blood pressure in man. Digest. Int. Conf. on Med Biol Eng, p. 104. Reeben V & Epler M (1983) Indirect continuous measurement of mean arterial pressure. Advanges in Cardiovascular Physics 5, Cardiovasc Eng Part II, pp. 90–118. Rhee S, Yang B-H & Asada, HH (2001) Artifact-resistant power-efficient design of finger-ring plethysmographic sensors. IEEE Trans Biomed Eng 48(7): 795–805.

82 Rhee S, Yang B-H, Chang K & Asada, HH (1998) The ring sensor: a new ambulatory wearable sensor for twenty-four hour patient monitoring. Proc. 20th Annual International Conference of the IEEE-EMBS, Hong Kong, China 20(4): 1906–1909. Rogueiro-Gómez A & Pallás-Areny R (1998) A new method for automated blood pressure measurement, Physiol Meas 19: 205–212. Ruha A (1998) Detection of Heart Beat in Ambulatory Heart Rate Measurements, Methods and Integrated Circuits. Oulu, Finland: Doctoral thesis, University of Oulu, Department of Electrical Engineering: 36–42. Safar ME, London GM, Asmar R & Frohlich ED (1998) Recent advances on large arteries in hypertension. Hypertension 32: 156–161. Šantić A & Lacrović I (1999) Simultaneous application of multiple oscillometric methods for blood pressure measurement in finger. Proc. First Joint BMES/EMBS Conference Serving Humanity, Advancing Technology, Atlanta, GA, USA: 231. Šantić A & Šaban M (1997) Two methods for determination of diastolic pressures in fingers. 1995 IEEE-EMBC and CMBEC, Theme 1, Cardiovascular System, Montreal, Que, Canada, 1: 149– 150. Šantić A, Štritof T & Bilas V (1998) Plethysmography measurements using short current pulses with low-duty cycle. Proc. 20th Annual International Conference of the IEEE, 20(4): 1889–1892. Sasayama S, Imachi Y, Yagi T, Imachi K, Ono T & Man-i M (1994) Development of optoelectronic monitoring system for ear arterial pressure waveforms. Proc. SPIE-The International Society for Optical Engineering 2084: 354–361. Sato T, Nishinaga M, Kawamoto A, Ozawa T & Takatsuji H (1993) Accuracy of the continuous blood pressure monitor based on arterial tonometry. Hypertension 21: 866–874. Savolainen A & Kirjavainen K (1989) Electromechanical film. Part 1. Design and characteristics. J Macromolecular Science-Chemistry, 26: 583–591. Savolainen A (1990) Biaxially oriented polypropylene blown films. I Morphological analysis of orientation in the machine direction. Polymer Engineering and Science 30: 1258–1264. Schiller Z & Pasch T (1991) Servo-plethysmo-manometry for continuous noninvasive blood pressure monitoring. Anaesthesist 40(2): 105–109. Schmidt TF, Wittenhaus J, Steinmetz TF, Piccolo P & Lüpsen H (1992) Twenty-four-hour ambulatory noninvasive continuous finger blood pressure measurement with PORTAPRES: a new tool in cardiovascular research. J Cardiovasc Pharmacol 19 Suppl 6: S117–145. Sebald DJ, Bahr DE & Kahn AR (2002) Narrowband auscultatory blood pressure measurement. IEEE Trans Biomed Eng 49(9): 1038–1044. Segers P, Carlier S, Armstrong G, Verdonck P & Thomas J (1998) In vivo application of the generalised radial-aorta and carotid-aorta pressure transfer function. Proc. Computers in Cardiology, Cleveland, OH, USA 1: 669–672. Shaltis P, Reisner A & Asada H (2005) Calibration of the Photoplethysmogram to Arterial Blood Pressure: Capabilities and Limitations for Continuous Pressure Monitoring. IEEE-EMBS, Proc. 27th Annual International Conference, Shanghai, China: 3970–3973. Shaltis P, Wood L, Reisner A & Asada H (2005) Novel Design for a Wearable, Rapidly Deployable, Wireless Noninvasive Triage Sensor. IEEE-EMBS, Proc. 27th Annual International Conference, Shanghai, China: 3567–3570. Siivola J, Leinonen K & Räisänen L (1993) ETMF-polymer transducer as a detector of respiration in humans. Med Biol Eng Comput 31: 634–635. Silke B & McAuley D (1998) Accuracy and precision of blood pressure determination with the Finapres: an overview using re-sampling statistics. J Hum Hypertens 12: 403–409. Sorvoja HSS & Myllylä R (2003) Accuracy of the electronic palpation method. Proc. 7th Conference of the European Society for Engineering in Medicine (ESEM), Halle, 1: 196.

83 Stewart WR, Ramsey MW & Jones CJH (1994) Non-invasive measurement of pulse wave velocity using transputer-based analysis of Doppler flow audio signals. Physiol Meas 15: 299–308. Streicher, Muller R, Peremans H & Lerch R (2003) Broadband ultrasonic transducer for an artificial bat head. Proc. IEEE International Ultrasonic Symposium, 2: 1364–1367. Teng XF & Zhang YT (2003) Continuous and noninvasive estimation of arterial blood pressure using a photoplethysmographic approach. IEEE-EMBS, Proc. 25th Annual International Conference, Cancun, Mexico, 4: 3153–3156. Terry S, Eckerle J, Kornbluh R, Low T & Ablow C (1990) Silicon pressure transducer arrays for blood-pressure measurement. Sensors and Actuators A21–A23: 1070–1079. Triedman JK & Saul JP (1994) Comparison of intra-arterial with continuous noninvasive blood pressure measurement in postoperative pediatric patients. J Clin Monit 10(1): 11–20. Ursino M & Cristalli C (1994) Mathematical analysis of the oscillometric technique for indirect blood pressure evaluation. Engineering Advances: New Opportunities for Biomedical Engineers, Proc. 16th Annual International Conference of the IEEE-EMBS 2: 1286–1287. Ursino M & Cristalli C (1996) A mathematical study of some biomechanical factors affecting the oscillometric blood pressure measurement. IEEE Trans Biomed Eng 43(8): 761–778. van Popele NM, Bos WJW, de Beer NAM, van der Guip DAM, Hofman A, Groppee DE & Wittemann JCM (2000) Arterial stiffness as underlying mechanism of disagreement between an oscillometric blood pressure monitor and a sphygmomanometer. Hypertension 36(4): 484–488. van Popele NM, Grobbee DE, Bots ML, Asmar R, Topouchian J, Reneman RS, Hoeks APG, van der Kuip DAM, Hofman A & Witteman JCM (2001) Association between arterial stiffness and atherosclerosis: the Rotterdam study. Stroke 32: 454–460. Vieri-Gashi E, Kärjä-Koskenkari P, Nissilä S, Sorvisto M, Sorvoja H & Myllylä R (2000) Blood pressure variation measured by the electronic palpation method and compared to intra-arterial variation. Proc. Işik 2000 Workshop on Biomedical Information Engineering, Istanbul, Turkey: 101–103. Vieri-Gashi E, Sorvoja H, Myllylä R, Nissilä S, Sorvisto M, & Kärjä-Koskenkari P (2001) The effect of the venous pressure to the blood pressure signals measured by the palpation method. Proc. 23rd Annual International Conference of the IEEE-EMBS, Istanbul, Turkey 4: 3259–3261. Wang J-J, Lin C-T, Liu S-H & Wen Z-C (2002) Model-based synthetic fuzzy logic controller for indirect blood pressure measurement. IEEE Trans Systems, Man and Cybernetics, Part B 32(3): 306–315. Weaver, C S, Eckerle J S, Newgard P M, Warnke C T, Angell J B, Terry SC & Robinson J (1978) A study of noninvasive blood pressure measurement technique. Noninvasive Cardiovascular Measurements. Soc Photo-optical Instr Eng 167: 189. Wegener M & Gerhard-Multhaupt R (2003) Electric poling and electromechanical characterization of 0.1-mm-thick sensor films and 0.2-mm-thick cable layers from piezoelectric poly(vinylidene fluoride-trifluoroethylene). IEEE Trans Ultrason Ferroelect Freq Contr 50(7): 921–931. Wegener M, Paajanen M, Wirges W & Gerhard-Multhaupt R (2002) Corona-induced partial discharges, internal charge separation and electromechanical transducer properties in cellular polymer films. Proc. 11th International Symposium on Electrets: 36–45. Wegener M, Wirges W, Gerhard-Multhaupt R, Paajanen M, Minkkinen H & Raukola J (2003) Enhancing the cellular structure and the electromechanical response of the ferroelectrets –gas diffusion expansion of voided polypropylene films. Proc. Annual Report Conference on Electrical Insulation and Dielectric Phenomena: 36–39. Wesseling KH (1996) Finger arterial pressure measurement with Finapres. Z Kardiol 85 Suppl 3: 38–44.

84 Williams B, Poulter NR, Brown MJ, Davis M, McInnes GT, Potter JF, Sever PS, Thom SM, BHS guidelines working party, for the British Hypertension Society (2004) British Hypertension Society guidelines for hypertension management 2004 (BHS-IV): summary. BMJ 328: 634– 640. Wood L & Asada H (2005) Active Motion Artifact Reduction for Wearable Sensors Using Laguerre Expansion and Signal Separation. IEEE-EMBS, Proc. 27th Annual International Conference, Shanghai, China 1: 3571–3574. Yang B-H; Rhee S & Asada HH (1998) A twenty-four hour tele-nursing system using a ring sensor. Robotics and Automation, Proc. IEEE International Conference 1: 387–392. Yelderman M & Ream A K (1979) Indirect measurement of blood pressure in the anesthetized patient. Anesthesiol 50: 253–256. Zheng DC & Zhang YT (2003) A ring-type device for the noninvasive measurement of arterial blood pressure. IEEE-EMBS, Proc. 25th Annual International Conference, Cancun, Mexico 4: 3184–3187. Zorn E A, Wilson M B, Angel J J, Zanella J & Alpert B S (1997) Validation of an automated arterial tonometry monitor using Association for the Advancement of Medical Instrumentation standards. Blood Press Monitor 2(4): 185–188. Elvan-Taspinar A, UiterKamp LAMS, Sikkema JM, Bots ML, Koomans HA, Bruinse HW, Franx A. Validation and use of the Finometer for blood pressure measurement in normal, hypertensive and pre-eclamptic pregnancy J Hypertension 21: S2053-2060.

Original papers I

Ruha, A, Miettinen J, Sorvoja H & Nissilä S (1996) Heart rate measurement based on detection of the radial artery pressure pulse. Proc. BIOSIGNAL’96, Brno, Czech Republic, 1: 198–200.

II

Sorvoja H, Kokko V-M, Myllylä R & Miettinen J (2005) Use of EMFi as a blood pressure pulse transducer, IEEE Transactions on Instrumentation and Measurement 54(6): 2505–2512.

III

Nissilä S, Sorvisto M, Sorvoja H, Vieri-Gashi E & Myllylä R (1998) Noninvasive blood pressure measurement based on the electronic palpation method. Proc. 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, USA, 20(4): 1723–1726.

IV

Sorvoja H, Myllylä R, Nissilä S, Kärjä-Koskenkari P, Koskenkari J, Lilja M & Kesäniemi YA (2001) A method to determine diastolic blood pressure based on pressure pulse propagation in the electronic palpation method, Proc. 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, 4: 3255–3258.

V

Sorvoja H, Myllylä R, Nissilä S, Kärjä-Koskenkari P, Koskenkari J, Lilja M & Kesäniemi YA (2001) Systolic blood pressure accuracy enhancement in the electronic palpation method using pulse waveform, Proc. 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, 1: 222–225.

VI

Sorvoja H, Hast J, Myllylä R, Kärjä-Koskenkari P, Nissilä S & Sorvisto M (2003) Blood pressure measurement method using pulse-transit-time. Molecular and Quantum Acoustics 24: 169–181.

VII Sorvoja H & Myllylä R (2004) Accuracy of the electronic palpation blood pressure measurement method versus the intra-arterial method. Technology and Health Care, International Journal of Health Care Engineering 12(2): 145–146. VIII Sorvoja H, Myllylä R, Kärjä-Koskenkari P, Koskenkari J, Lilja M & Kesäniemi YA (2005) Accuracy comparison of oscillometric and electronic palpation blood pressure measuring methods using intra-arterial method as a reference. Molecular and Quantum Acoustics 26: 235–260.

86 IX

Hast J, Myllylä R, Sorvoja H & Miettinen J (2002) Arterial pulse wave shape measurement using self-mixing effect in a diode laser, Quantum Electronics 32(11): 975–980.

Original publications are not included in the electronic version of the dissertation.

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