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Int. J. Biomedical Engineering and Technology, Vol. 10, No. 1, 2012
Fractal dimension of Electroencephalogram for assessment of hypnosis state of patient during anaesthesia Sanjeev Kumar* and Amod Kumar Biomedical Instrumentation Unit, Central Scientific Instruments Organisation Sector 30-C, Chandigarh-160030, India E-mail:
[email protected] E-mail:
[email protected] E-mail:
[email protected] *Corresponding author
Satinder Gombar Department of Anaesthesia & Intensive Care Government Medical College & Hospital Sector 32, Chandigarh, India Email:
[email protected]
Anjan Trikha Department of Anesthesiology, All Indian Institute for Medical Sciences, Ansari Nagar, New Delhi-110029, India Email:
[email protected]
Sneh Anand Centre for Biomedical Engineering, Indian Institute of Technology, Hauz Khas, New Delhi- 110016, India Email:
[email protected] Abstract: Depth of Anaesthesia (DoA) measurement and control is a demanding task that must be done to avoid intraoperative awareness and explicit recall of pain during surgery. Conventional methods of assessing DoA involve monitoring of physiological parameters, which are not found reliable, as patient awareness during surgery with anaesthetic agents has been reported. Electroencephalogram (EEG) is found to be a reliable means to determine the real anaesthetic state of a patient during surgery. Balanced anaesthesia is the fusion of four different components: hypnosis, analgesia, amnesia and neuromuscular blockade. The accurate control of anaesthesia is possible only with the accurate assessment of the different components of anaesthesia. The major component of balanced anaesthesia is hypnosis, which gives the level of unconsciousness of the patient during surgery. In the present study, efforts were made to calculate and validate
Copyright © 2012 Inderscience Enterprises Ltd.
Fractal dimension of Electroencephalogram an EEG-based parameter which is able to predict the awake and anaesthetic sleep state of the patient. In the present study, the EEG of 60 patients were recorded during normal awake state and while under anaesthesia. Analysis of the EEG signals was performed by non-linear quantifiers. Higuchi’s Fractal Dimension (HFD) has been calculated for this recorded EEG waveform for all patients in both states. It was found that HFD is able to predict the awake/sleep state of the patient quite accurately. Validation of the study was done by monitoring BIS in parallel and concluded that the HFD of the EEG goes down as the patient moves into deep hypnosis state. Keywords: electroencephalogram; balanced anaesthesia; hypnosis; higuchi fractal dimension; BIS monitoring. Reference to this paper should be made as follows: Kumar, S., Kumar, A., Gombar, S., Trikha, A. and Anand, S. (2012) ‘Fractal dimension of Electroencephalogram for assessment of hypnosis state of patient during anaesthesia’, Int. J. Biomedical Engineering and Technology, Vol. 10, No. 1, pp.30–37. Biographical notes: Sanjeev Kumar is B.Tech in Electronics & Telecommunication Engineering from College of Engineering Roorkee in 2004 and pursuing Ph.D. in Biomedical Engineering from Indian Institute of Technology Delhi. He has about 5 years R&D experience in the area of Bio-Medical Instrumentation. He has about 8 publications in reputed journals. He has been working on project of national importance like Myoelectric Arm, Intelligent Prosthetic Device Development and Depth of Anesthesia Monitor (DoA) and Intelligent Diagnostic Bench for rural Healthcare based on Ancient Medical Practices. Amod Kumar is B.E. (Hons.) in Electrical and Electronics Engineering from Birla Instituteof Technology and Science, Pilani (Raj.) in 1979; M.E. in Electronics from Punjab University, Chandigarh and Ph.D. from IIT Delhi. He has about 30 years experience in Research and Development of different instruments in the areas of Process Control Instrumentation, Biomedical Engineering and Prosthetics. He is currently working as senior scientist in Central Scientific Instruments Organisation, Chandigarh. He has about 13 publications in reputed journals. He visited Germany under DAAD fellowship in 1987-88. His areas of interest are Embedded System Design, Digital Signal Processing and Soft Computing. Satinder Gombar is Graduated from Lady Hardinge Medical College, New Delhi and did MD in Anaesthesia & Intensive Care from PGIMER Chandigarh. She was awarded silver medal for the order of first merit during MD Anaesthesia examination. She had been President of the Chandigarh branch of Indian Society of Anaesthesiologists from 2008-2011. Presently she is member of the editorial board of Indian Journal of Pain. She has been invited as a guest reviewer for peer review of the manuscripts submitted to various journals. Her special interests are in the fields of Paediatric and Obstetric Anaesthesia, Ultrasound and Intensive Care. Anjan Trikha is Graduated from University College of Medical Sciences, New Delhi and Masters in medicine from PGIMER Chandigarh. Invited as a faculty for teaching programmes in many parts of the country and Malaysia. Worked as a full time consultant for 2 years in Wellington Public Hospital, Wellington, New Zealand and presently he is the President of Association of Obstetric Anaesthesiologists, Editor of Journal of Obstetric Anaesthesia and Critical Care and on editorial board of Indian Journal of Anaesthesia and Journal of Anaesthesia and Clinical Pharmacology. His Special interests are in the areas of: Simulation and Anaesthesia, Difficult Airway, Regional anesthesia including obstetrical anesthesia and Intensive Care.
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S. Kumar et al. Sneh Anand is Graduated in Electrical Engineering from Punjabi University, Patiala in 1970 and did her Masters and Doctoral Research from Indian Institute of Technology Delhi in 1972 and 1976 respectively. She is a Fellow of INAE, IETE and IMPA. Her research and development activities are in the areas of transducers and biosensors, signal processing, reproductive bioengineering, electrically enhanced drug delivery and rehabilitation engineering. With her dedicated R&D efforts she has published more than hundred papers in reputed biomedical journals. She has over ten inventions and five patents to her credit and has transferred several know-hows to Indian industry.
1
Introduction
Anaesthesia has been defined as that state which ensures the suppression of the somatic and visceral sensory components and thus, the perception of pain (Prys-Roberts, 1987). To avoid intraoperative awareness and explicit recall of pain, it is essential to measure the Depth of Anaesthesia (DoA) of a patient and to control it accurately and as precisely and objectively as possible. In conventional methods, DoA assessment is based on empirical measurement of physiological parameters such as heart rate, blood pressure, respiration pattern, etc. However, patient awareness during surgery has been reported, and it is a major clinical concern regarding anaesthesia. (Winterbottom, 1950) Further, these physiological parameters are indirectly affected by other drugs, besides showing patient to patient variation. As anaesthetic agents have significant effects on the activity of the Central Nervous System (CNS), it is advocated that the Electroencephalogram (EEG) can provide a reliable basis for deriving a surrogate measurement of anaesthesia. (Soltero et al., 1951; Neigh et al., 1971; Long et al., 1989; Katoh et al., 1998; Thomsen and Prior, 1996). Anaesthesia is defined as the optimal fusion of four distinct components: hypnosis – unconsciousness of the patient in the surgical zone; analgesia – blocking of pain sensation; amnesia – temporary memory loss so that the patient does not remember events that happened during the surgery and neuromuscular blockade – driving the patient into a paralysed state so that he does not move during surgery (Woodbridge, 1957; Guedel, 1937; Kissin, 1993; Glass et al., 1997; Judith et al., 2002; Stuart et al., 1988). In the present study, evaluation of the major component of anaesthesia, i.e., hypnosis, has been done. The main objective of the study was to observe changes in the EEG when the patient moves from a normal awake state to an anaesthetic sleep state. EEG data was recorded from patients in both normal awake and anaesthetic sleep states. Higuchi fractal dimension (HFD) was calculated for EEG data for both states, and it is found that HFD of two different events is able to predict the awake and anaesthetic sleep states of the patient. Validation of the obtained results is done by BIS index monitoring parallel to the study.
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Materials and methods
After obtaining ethical clearance from the institutional ethics committee of the hospital and written informed consent from the patients, 60 healthy patients (34 males and 26 females) of age between 20 and 60 years (35.08 ± 15.08) and weighing between 40 and 85 kg, who
Fractal dimension of Electroencephalogram
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were scheduled to undergo non-thoracic, non-vascular and non-neuro surgical procedures of expected duration 1–2 h requiring General Anaesthesia (GA) were included in the study. Patients with significant cardio-respiratory illness, of less than 70% or more than 130% of ideal body weight, or on sedative/analgesic/psychoactive drugs including alcohol were excluded from the study. The selected patients were kept fasting for 6 h prior to surgery and were premedicated with tablet alprazolam 0.25 mg on the morning of surgery. Before induction of GA, the baseline EEG data was recorded. Disposable EEG electrodes were placed according to the 10–20 lead system. Two channel (g.tec Bio-amplifier AD Instrument, Australia) recording was done taking Fp1 and Fp2 as active electrodes and Cz as a reference electrode. The ground electrode was connected at the Nasion. Although two channel recording was done, for processing only one channel was used and other was kept for backup. The data was recorded in two different sessions: Session 1:
Normal awake state EEG data taken as baseline EEG
Session 2:
Anaesthetic sleep state EEG data when the patient was fully anaesthetised
Once the patient was in the Operating Room (OR), baseline haemodynamic parameters like Heart Rate (HR), Non-invasive Blood Pressure (NIBP) and arterial oxygen saturation (SPO2) were recorded (S/5 Datex Ohmeda, USA) and continuously monitored throughout the surgery. An intravenous (IV) line was started with 18 G cannula with normal saline. Patients were pre-oxygenated for 3 minutes, and then general anaesthesia was induced using IV propofol 2–3 mg kg–1, and fentanyl 2 µgkg–1. Vecuronium 0.1 mg kg–1 was used to facilitate endotracheal intubation using an appropriately sized cuffed endotracheal tube (PVC Portex). Anaesthesia was maintained using halothane, N2O in O2 (60:40) with a fresh gas flow of 3L per minute to maintain an end tidal CO2 of 35 mmHg using a closed circuit with circle absorber (Aestiva 5TM, Datex Ohmeda, USA). The anaesthetic state of the patient was assessed by connecting a BIS Vista monitor (Aspect Medical Systems, Newton, MA). The BIS-QUATTRO Sensor TM, composed of self-adhering flexible bands holding four electrodes, was applied to the forehead with a frontal-temporal montage. BIS monitoring was done in parallel with the EEG recording. As the patient was fully anesthetised, the recording of anaesthetic sleep EEG data was started. After the completion of surgical procedure, the residual neuromuscular blockades were antagonised with IV glycopyrrolate 0.01 mg kg–1 and neostigmine 0.04 mg kg–1. The patient was then extubated and shifted to the Post-Anaesthetic Care Unit (PACU) for further monitoring and care.
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Processing and analysis of EEG data
The raw EEG data were visually inspected and motion artifacts-free EEG data was chosen for further processing. Artifacts such as line frequency, eye blink, etc. were removed using house-coded algorithms in Matlab. A ten-minute duration of filtered EEG data from the normal awake state and the anaesthetic sleep state was considered for analysis. Higuchi proposed a method which gives fractal dimension in the time domain without the necessity for embedding in a phase space, thus permitting an immediate link between EEG variations and complexity changes. The algorithm is very simple and readily
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applicable for real time signals of short duration, non-stationary and noisy (Higuchi, 1988). Higuchi’s method is based on a different measure of the length of the signal, i.e., estimating the length at different time intervals. For a given time series x(1), x(2), x(3)… .x(N) of data to be analysed, new time series were constructed for m = 1, 2, ¼ k Xkm = {x (m), x (m + k), x (m + 2*k) … x [m + int ((N–m)/k]*k)} where ‘m’ is the initial data point and ‘k’ is the discrete time interval to select the subsequent data points. In this study, ‘k’ was chosen as six (Accardo et al., 1997). For each new time series constructed, its average length Lm (k) is defined as Lm (k ) =
N −m k
∑ i =1
N − m x(m + ik ) − x(m + (i − 1)k ) (n − 1) / k k
where ‘N’ is the total length of the data sequence and (N − 1)/(int ((N − m/k)) k) is a normalisation factor. An average length is computed for all time series, as the mean of the lengths for m = 1, 2, 3 ¼ k. This procedure is repeated for each time series ranging from 1 to Kmax. The mean length of the original time series is calculated as the average of Lm(k): L(k ) =
1 k ∑ m =1 k
Lm (k ).
Since L(k) is proportional to k–D for a fractal time series, the Fractal Dimension (FD) of the signal was obtained in this study as the slope of the curve ln (L(k)) vs. ln (1/k) using the least-squares linear best fitting method. The HFD of all 60 patients was calculated for both awake and anaesthetic sleep conditions for the ten minutes’ duration of EEG data and plotted. BIS values were also plotted in parallel.
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Results
Ten-minute EEG data for each patient from each session of the normal awake state and the anaesthetic sleep state were considered and HFD for each session was calculated and plotted. Consistent changes for all patients in HFD were found in the EEG during awake and anaesthetic sleep states. It was observed that the value of HFD went down from awake to anaesthetic sleep condition for all 60 patients. The validation of the calculated results was done by plotting corresponding values of BIS. Figure 1 shows the typical plots of HFD along with the changes in BIS for 5 randomly selected patients.
Fractal dimension of Electroencephalogram Figure 1
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Higuchi’s Fractal Dimension for 5 randomly selected patients with corresponding BIS values
Conclusion and discussion
In recent years, a numbers of devices, such as the BIS Monitor (Aspect Medical Systems Inc.), Narcotrend Monitor, Entropy Monitor (Datex), etc. have become available for measurement of the hypnosis component of balanced anaesthesia. The Bispectral Index (BIS) monitor from Aspect Medicare System, USA is a very popular DoA monitor which gives the awareness index of the patient in a quantitative manner, with 100 meaning fully awake
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and 0 meaning asleep. It calculates the index on the basis of three EEG parameters – β-ratio, SyncFastSlow (SFS) and BSR/QUAZI (Rampil, 1998). Since burst separation occurs only in a deep plane of anaesthesia, the awareness index given by BIS is based on only two EEG parameters, β-ratio and SFS. In the present study, the calculated results (HFD) are compared with the BIS Index. Scope of improvement is always required in sophisticated biomedical instruments. Using the calculated HFD, which gives clear differentiation between normal awake and anaesthetic sleep states, EEG data may be taken into account to calculate the real anaesthetic state of the patient. The conclusions of this experimental study are: •
To analyse EEGs signal in the time domain for estimating the length of EEG at different time intervals, HFD has been proved to be a good parameter to predict the awake and anaesthetised states of the patient.
•
A reliable and more accurate depth of hypnosis index may be computed by combining the calculated HFD together with the existing indices of DoA.
Computing the hypnosis index by combining HFD together with the existing indices of DoA will improve the accuracy of the amount of anaesthetic drug to be delivered to the patient that will result in economical anaesthetic procedure. This also means more number of patients can be handled, due to faster recovery time. The limitation of the present study is the number of patients analysed is low. Future direction is to analyse a bigger number of patients to create a database of EEG with calculated HFD which will increase the accuracy of the system further.
Acknowledgements We would like to thank the Council of Scientific and Industrial Research (CSIR), New Delhi (India) for giving the opportunity and funding support to carry out this study.
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