Young-sik Seo. 1 ... in the closed loop provide treatment strategies for optimization to reach a ... will enable more precise diagnosis and prognosis of diseases.
12 Annual IEEE Wireless and Microwave Technology Conference (WAMICON Clearwater, FL, April 18-19, 2011 Invited review paper
Wireless Implants for in vivo Diagnosis and Closed-loop Treatment J.-C. Chiao1,2, Aydin Farajidavar2, Hung Cao1, Philip McCorkle1, Manthan Sheth1, Young-sik Seo1, Tim Wiggins1, Shreyas Tharkar1, Sanchali Deb1, Smitha M.N. Rao1 1
Electrical Engineering, 2Bioengineering, University of Texas at Arlington, Arlington, TX
Abstract — In this presentation, we will review and discuss recent advances in the research of wireless telemetry for medical applications in our group at UT-Arlington, particularly those based on a similar platform for in vivo monitoring of physiological parameters. System for recording ECoG signals in brain, recording in vivo gastric myoelectric activities in stomach, sensing in vivo strain variations in bladder, and detecting reflux episodes in esophagus have been demonstrated. These systems consist of passive transducers for physiological signal transduction and an active transceiver for signal relay and recording. The real-time in vivo physiological signal acquisition and related neuro-/gastro-stimulation form a closed loop between the human body and control electronics. Continuous feedback mechanisms that could be implemented in the closed loop provide treatment strategies for optimization to reach a desired comfort level for individual patient. The wireless systems quantitatively document symptoms and associated physiological signals over a long term while allowing the patients to resume regular daily activities. This will enable more precise diagnosis and prognosis of diseases for the caregivers.
I. INTRODUCTION Information technologies have been utilized greatly to increase care efficiency, quality and efficacy in hospitals for patient information management, drug and equipment inventory, scheduling and staffing. However, major bottlenecks still exist, as caregivers are required to physically interact with patients for sampling and acquisition of physiological parameters. The caregivers can only handle a limited number of patients within certain intervals, and the sampling can only be carried out during the interaction period bearing limited numbers of visits. Better care with higher diagnosis accuracy can be provided, if more and time-lapsed data can be obtained without causing patients discomfort or limiting mobility. Meanwhile, patient data documentation has become too cumbersome. The lack of portability and timely accessibility of the physiological information further prevents real-time management by caregivers and/or patients. Wireless technologies bring promising solutions to the aforementioned issues. Wide use and deep penetration of lowcost portable electronics and wireless devices have made a significant impact to our societies. A wearable module associated with a chronic implant based on wireless signal transduction could be implemented to acquire patients’ physiological data in long term while they resume normal activities without physical constrains. Wireless communication can be further utilized to transmit the information to clinics in 1|Page
real time or store the massive data for future off-line diagnosis. Patients will also gain access to interact with or manage their own treatment methods such as adjusting neuro- or gastrostimulation doses according to their own subject feeling or comfort level. The result will reduce the workload for the health providers and thus the costs since most of the patients can be monitored at home or work. The care outcomes will be better as the system empowers patients to manage their own chronic illness and enables quantitative documentation of symptoms for more precise diagnosis. The proposed system architecture includes a passive transducer for signal transduction, a wearable module to record and relay signals, and a receiver base station for data acquisition, as shown in Fig. 1. The passive transducer architecture allows a batteryless sensor to be implanted inside the body enabling chronic sensing and recording. The batteryless device architecture also allows energy transfer across tissues for electrical stimulation of the target organ or
Base Station Implant
Wearable unit Figure 1. The concept of the wireless in vivo diagnosis and closed-loop treatment system. Base station
Transceiver module RF powering Transceiver µC
Envelope detector Power amplifier Signal generator Wearable unit
Sensor / stimulator
Tissue Data transmission
Figure 2. The in vivo signal transduction system blocks.
Wearable unit (a)
(b) (c) Figure 3. (a) Block diagram of the wireless system for neurorecording, including the wearable unit and a base station. Dashed line #1 shows the transmitted packets containing neuronal signals, and #2 indicates the acknowledgement in the opposite direction. (b) The graphical user interface (GUI). (c) Top view of the assembled wearable device.
tissues that could be used for management of symptoms. The wearable module serves as a relay for continuous acquisition and transmission of the essential signals to the receiving station, in which a large database can store the information for further off-line diagnosis. Meanwhile, a control device for the patient could potentially help to manage his/her own conditions, either to adjust the stimulation doses or to set the control algorithms for automatic management. In this paper, we will discuss a few examples of in vivo diagnosis and prognosis applications utilizing the proposed system. II. SYSTEM DESIGN The in vivo system blocks are illustrated in Fig. 2. The RF powering from the wearable unit to the implant utilized an operating frequency of 1.3 MHz with a duty cycle of 30%. The sensor signal transduction was conducted with loadmodulating at the same operating frequency. Generally speaking, the carrier frequency is not limited to be at 1.3MHz. The passive implant consists of a coil antenna (L2), a capacitor (C2), a switch, an energy harvesting circuit, a relaxation oscillator and a sensor. The antenna and tuning capacitor were tuned to the resonant frequency. A voltage multiplier to amplify the harvested DC energy was utilized with a series of diodes and capacitors to increase the transduction distance. A relaxation oscillator circuitry was used to convert the changes in potential, capacitance or resistance of the sensor to frequency variations. The switch modulated the carrier frequency with the modulated frequency generated by the relaxation oscillator. 2|Page
The wearable unit consists of a coil antenna (L1) and capacitor C1 which were chosen for resonance at the carrier frequency, a class-E power amplifier, and an envelope detector to read the load-modulation signals. The demodulated signals were fed into a microprocessor. The communication between the wearable unit and the base station was based on eZ430RF2500 (Texas Instrument) or NRF24Le1 (Nordic Semiconductor) modules. The MSP430 in the eZ430RF2500 module and the microprocessor in the NRF24Le1 module were programmed to process the modulated signals. The signals could be digitized by counting the number of pulses in a short duration. The data was loaded into the data packet and transmitted wirelessly to the base station. The base station consists of an eZ430RF2500 or NRF24Le1module and interfaces. The received data was sent to a computer by the microcontroller through a serial port and displayed in real time with a graphical user interface (GUI) programmed in Labview. The data is also logged into text files for off-line diagnosis. III. NEURORECORDING AND NEUROSTIMUALTION The wearable unit consistes of an analog board, and NRF24Le1, which combines an ADC, a microcontroller (µC) and a 2.4-GHz transceiver, as shown in Fig. 3(a). The analog board conditions the signals through a two-stage amplification and band-pass filtration. Signals pass through an instrumentation amplifier INA333 (Texas Instruments) then to a second-order band-pass filter to eliminate noises before the next stage of amplification. The gains and filter passbands depend on respective applications. The amplified signals are sampled, digitized, put into data packets and sent by the transceiver. Packets are received at the receiving station and sent to a computer via serial communication where a graphical user interface (GUI) designed in Labview (National Instrument) displays the data in parallel panels (Fig. 3(b)). After the microcontroller acquires data from ADC and loads it into the radio, the radio turns into the transmitting mode, which takes 130 μs, and transmits the data packets. Then the radio takes another 130 μs returning back to the receiving mode while the radio in the base station which is in the receiving mode looks for the data. If it receives the packet, it turns into the transmitting mode and sends back an acknowledgement to the wearable unit while loading the data on the UART (universal asynchronous receiver/transmitter, at 500 kBaud) to be sent to the computer as the microcontroller in the wearable unit loads the next set of data. If the data packet is lost without the acknowledgement, the radio in the wearable unit re-transmits the data packet to the base station until attainment is verified. The re-transmitting procedure mainly depends on the sampling rate of the microprocessor and the time for each packet to travel in air, which varies across applications. Each packet is composed of 1 preamble byte, 3 to 5 address bytes, up to 32 bytes of payload and 1 to 2 bytes for cyclic redundancy check (CRC). The time on air TOA can be
Figure 4. ECoG signals recorded by a conventional wired (top) and our wireless (bottom) systems. Only a section of 7.3 seconds was shown. The signals were identical. (a)
Figure 5. Two minutes of GEA slow wave signals recorded by (a) wired and (b) wireless systems.
calculated from TOA = (Packet length)/(Air data rate). The air data rate was chosen to be 1 Mbps and the packet length differed in each application. The following examples explain the detailed specifications on ECoG and GEA systems that were designed, assembled and tested in animal models. ECoG: Electrocorticography (ECoG) has conventionally provided valuable information on the mechanisms of brain activities [1, 2]. ECoG acquires signals from the surface of the dura in the brain, therefore providing a better spatial resolution, broader bandwidth and higher characteristic amplitude than conventional electroencephalography (EEG). ECoG is more robust to motion artifacts which are the main source of noises in freely behaving/moving subjects. The analog board on the ECoG amplified the signals at 74 dB and filtered the undesired signals out of the range of 1 – 150 Hz. The sampling rate for this system was 1 kSps for each of the seven channels. The number of payloads was chosen as 4 bytes; hence, the TOA was calculated as 0.096 ms at its maximum. In vivo ECoG signals were successfully recorded from a rodent model for several hours with our wireless system and a 1401Plus unit (CED, Inc) wired system as the comparison 3|Page
control. Rodents were implanted under approved surgery protocols with stainless steel screws in the somatosensory cortex as active electrodes and two over the cerebellum as the ground and reference electrodes. The similarity of the acquired data is shown in the 7.3 seconds of the recorded signals plotted in Fig. 4. The wireless recording enables the possibility for a closedloop feedback mechanism applying the recorded neuronal signals that indicate brain activities such as nociception as inputs of the feedback to determine the sequential neurostimulation parameters for inhibition as outputs. A wireless neurostimulator using the same platform that can generate bipolar pulses with adjustable voltage levels could be added to the ECoG system to form a closed loop between the human body and machine. The two-way communication between transceivers closes the loop with one connected to a computer through a serial port to record ECoG signals transmitted from the wearable unit and send out required therapeutic parameters generated by a Labview program to the wearable module. The microcontroller in the wearable module then converts the parameters into stimulation pulses that are fed to the electrodes. The closed-loop mechanism provides a possible means for real-time feedback to optimize neurostimulation doses in order to achieve a desired comfort level. GEA: In vivo gastric electrical activities (GEA) or myoelectric activities in the stomach consist primarily of very low amplitude and frequency signals termed slow waves . Recording slow waves across stomach provides important information for evaluating gastric motility disorders including gastroparesis. For optimal data, electrodes are placed invasively on the gastric wall with wires connecting through an abdominal incision or via the mouth of the patient to an electronic recorder. The wireless system reduces the wire connection from the human body to the acquisition equipment which currently limits the mobility of patients. The analog board for the GEA system was designed to amplify signals at 65 dB and filter the undesired signals out of the range of 0.05 – 0.3 Hz. Since the slow wave signal occurs at very slow frequencies, it was sampled at a rate of 8 Sps and transmitted via the payload with a size of 4 bytes. Therefore, the TOA is the same as the one in the ECoG system. For acquiring GEA signals, anesthetized canine models were used in which signals were acquired from serosal membranes of the stomach using flexible PCB electrodes. GEA slow wave signals were acquired in vivo from an animal model for 80 minutes, and the numbers of events recorded by a commercially-available standard system and the developed wireless system were identical. Figure 5 shows 2 minutes of the GEA signals recorded. Although the main purpose of our in vivo GEA recording system was designed to study stomach activities in order to elucidate the symptoms of gastroparesis, the recorded signals could be utilized to form a closed-loop feedback to determine the most suitable gastrostimulation pulse parameters applied
Bladder Sensor implant
Figure 6. The concept of the closed-loop urinary incontinence management system.
Figure 7. Measured real-time modulated frequencies in the strain sensor for various volumes in a bladder phantom model.
on mucosal or serosal tissues of the stomach to modulate the motility for individual patients. VI. PHYSIOLOGICAL PARAMETER MONITORING For physiological parameter monitoring systems, the communication between the wearable unit and the base station was based on two eZ430RF2500 modules. This module has an embedded MSP430 microcontroller, a 10/12 bit ADC, a CC2500 transceiver chip and a small on-chip antenna. The programmable CC2500 transceiver which has a working frequency range of 2.4−2.4835GHz supports a sleep mode (400 nA current consumption) with fast start-up time (240 μs from sleep to transmit or receive modes). This simple and lowpower protocol in eZ430RF2500 is also capable for both star and peer-to-peer networks. The MSP430 microcontroller supports a universal serial communication interface to communicate with PCs and two 16-bit timers which can be used in multiple-sensor applications. Incontinence management: Urinary incontinence (UI) is the involuntary loss of urine due to bladder control dysfunctions. Dysfunctions in urinary bladder cut off the sensory feedback to the central nervous system making the patient incapable of knowing when to induce urination and void the bladder. In long term, this can cause kidney damage [4, 5].
An interdigitated capacitive (IDC) strain sensor was micromachined from a 127-µm thick brass shim, followed by an encapsulation process to package the sensor in elastic polydimethylsiloxane. Passive telemetry was employed for the sensor in vivo in which the implant harvested the electromagnetic energy from the wearable unit, supplied power to operate the sensor and transduced the sensor data back to the reader in the wearable unit. The unit processed and transferred the signals to the base station connected to a computer which continuously displayed the strain of the bladder and recorded data for off-line diagnosis. The concept is shown in Fig. 6. Typically, the maximum volume of a human bladder is 550 ml and the volume at which patients should trigger voiding is about 400 ml. The sensor was calibrated and tested with a bladder phantom model. The strain sensor was designed to be attached on the serosal wall of the bladder. Measurement started when its volume reached beyond 390 ml. An alert message would be sent out at the 390-ml volume and the modulated frequency output indicated the strain/volume information. The volumes of 410, 430 and 450 ml, corresponding to strains of 1.68, 3.31 and 4.89%, generated modulated-frequencies at 12.487, 12.498 and 12.512 kHz, respectively. The real-time data obtained in the base station is shown in Fig. 7 which can be implemented with patient’s manual control for activation of sacral nerve stimulation to alleviate urinary incontinence. Gastroesophageal reflux disease (GERD): GERD refers to symptoms or tissue damage caused by the reflux of stomach contents, which may be acid or nonacid, into the esophagus and pharynx . GERD has been associated with esophageal cancers as the primary risk factor recognized. Therefore, monitoring the GERD symptoms comfortably and reliably becomes important for early diagnosis of esophageal cancers. Current methods utilizing tethered sensing probes limited patients’ activities and added stress to patients which affects detection accuracy. A wireless implant could potentially overcome the issues for patients in long-term diagnosis. An interdigitated electrode was designed for sensing impedance changes in the esophagus. It has 6 fingers that are 1.77 mm long, 177 µm wide and with a spacing of 177 µm. The impedance to frequency converter converted the impedance variations of electrode to frequency-varying signals with a relaxation oscillator. The electrode is represented by a variable resistor and a variable capacitor in parallel. The materials with low impedance such as acid or nonacid fluid have low resistance and high capacitance resulting in a highfrequency output. For high impedance material such as air, the resistance is high and the capacitance is low resulting in a lowfrequency output . Animal experiments were performed in pig cadavers to verify the feasibility. The experiments were performed on 6-8 months old pig cadavers (75 lbs each) with an average chest perimeter of 70 cm measured at the level of mid-sternum. An open gastrostomy was created through the anterior gastric wall in the body of the stomach. A gastroscope was advanced into
the stomach to remove excessive gastric fluid and content. The sensor implant was placed in the distal esophagus about 3cm proximal to the GE junction under direct endoscopic guidance. Several solutions were used to test the system including tap water, soft drink (Pepsi), orange juice with pulp (OJ(P)), orange juice with no pulp (OJ(N)), vinegar and simulated stomach acid. Table 1 shows the detected frequencies with different solutions in the first 17 episodes. The reflux episodes were clearly identified with the real-time frequency shifts from the baseline of 7.25 kHz and the episodes were correctly recorded in the base station computer. Table 1. Detected frequencies in different reflux episodes. Reflux episode 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
None Water Water Pepsi Pepsi Water OJ (P) OJ (P) Water OJ (N) OJ (N) Water Vinegar Vinegar Vinegar Water Acid Acid
7.25 8.28 8.15 9.03 9.15 8.48 9.30 9.33 8.55 9.35 9.38 8.48 9.38 9.55 9.36 8.60 11.68 11.43
CONCLUSIONS In this paper, we reviewed the research advances in several wireless systems designed for recording of ECoG signals in a closed-loop system designed for automatic inhibition of nociception, in vivo recording of gastric electrical activities for diagnosis of gastroparesis, in vivo monitoring of bladder volume for incontinence management, and in vivo sensing of gastroesophageal reflux episodes to diagnosis of GERD. These systems are based on a similar platform that consists of passive signal transduction for implants and active communication for signal recording and processing. These systems were verified in related animal models for their feasibility. The similar platform has potentials for many other applications that involve chronic monitoring of physiological signals for diagnosis and prognosis. ACKNOWLEDGEMENTS The authors would like to express their sincerely appreciation to National Science Foundation, Texas Advanced Research Program, Texas Instruments and Intel Corp. for their supports.
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