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Wireless implants for personalized medicine and chronic monitoring

By J.-C. Chiao

Wireless technology, in conjunction with nano devices, shows great promise for cost-effective implanted monitoring and management of patients.

The costs of healthcare have increased significantly and become major societal and financial issues. In the developing and underdeveloped countries, the quality of care and lack of means to care for a large population of patients are also serious problems. Continuous increase of aging population and several global epidemic events in the past years manifest dire needs to find cost effective means of diagnosis and monitoring for seniors and patients.

Wireless technologies bring promising solutions to these issues. Low-cost portable wireless electronics have made significant impacts in our societies. Currently, passive radio frequency identification (RFID) devices have been utilized in hospitals for patient information management, drug and equipment inventory, scheduling and staffing to improve efficiency. Aiming to reduce labor costs, improve diagnosis accuracy and move toward integrated healthcare solutions, major technical challenges still exist. Limited sampling of physiological parameters during visits to clinics provides incomplete information about the patients. Cluttered emergency rooms and added caregiver workload further complicate the situations. Better care in terms of higher diagnosis accuracy can be provided if time-lapsed data can be obtained without causing patients discomfort or limiting their mobility. Cumbersome documentation, lack of portability and timely accessibility of physiological data have also prevented real-time management of patients by either caregivers or patients themselves.

Combining with wireless technologies, recent advances in micro- and nano-technologies enable sensing functionalities and interfaces to human tissues. Miniaturization and low power consumption of devices benefit applications for chronic disease implantation allowing quantitative documentation of physiological, biochemical and behavioral parameters. The electrical interfaces to cells or tissues further provide direct stimulation or modification of functions with electrical currents for therapeutic treatment. This makes it possible to manage chronic diseases with a feedback mechanism from sensing to stimulation in a closed loop established between human and computer. With wireless communication, sensor networks for ubiquitous access to physiological information at various system levels, either within one’s body or within a group of patients, will be able to collect data for better deterministic and statistical understanding of such complex systems.

Although it is quite obvious that utilizing wireless techniques in medical devices and systems benefit their uses, the designs and requirements are often application-oriented and disease-specific, adding technical challenges in each case. Many groups have been demonstrating the aforementioned features in their works for various types of diseases [1,2]. For example, in our group, we have demonstrated an integrated wireless body network for chronic pain management [3, 4] and an endoscopically-implantable batteryless wireless device to monitor gastroesophagus reflux symptoms [5].

A wirelessly integrated closed loop consisting of neurorecorders to recognize pain signals and neurostimulators to electrically inhibit pain signals has been demonstrated in mechano-nociceptive neural activity models [3]. Recorder implants detect neuronal signals, amplify and transmit them wirelessly to an external, wearable module in which the adaptive algorithm filters, processes and classifies signals. The algorithm then makes a decision about the electrical stimulation dose parameters and the module wirelessly sends the command to the stimulator implant delivering electrical currents to neural tissues. The stimulation affects the operation of ion channels in neurons and thus pain signal propagation. The real-time feedback loop updates neuronal signals again, and the module iteratively decides the stimulation doses until the user-desired condition is met. Figure 1 shows an example of statistical results indicating that the system is able to detect the neuronal activity intensities and deliver trigger signals to the neurostimulator in brain according to a pre-set threshold in the closed-loop configuration and suppress excessive nociceptive activities in spinal cord dorsal horn neurons. The demonstration opens up the possibility of personalized medicine utilizing sophisticated and adaptive software with wireless neural implants in a body network to manage chronic pain. With functionalized electrodes with nanoparticle-modified surfaces on flexible probes made by microelectromechanical system (MEMS) techniques [6], the wireless closed-loop method can also be extended to the detection of neurotransmitters, which play critical roles in many neuro-disorders, as the signals to trigger electrical or optogenetic stimulation [7].

Figure 1: Twenty-six wide dynamic range (WDR) neurons in the left L5 region of spinal dorsal horn of rats were recorded to investigate the efficacy of automatic pain recognition and inhibition stimulation when the stimulation site was in the periaqueductal gray (PAG).
Figure 1: Twenty-six wide dynamic range (WDR) neurons in the left L5 region of spinal dorsal horn of rats were recorded to investigate the efficacy of automatic pain recognition and inhibition stimulation when the stimulation site was in the periaqueductal gray (PAG). Neuronal action potential (AP) responses (spikes/s) to different mechanical stimuli, innocuous or noxious, during the periods of control, stimulation, and recovery periods show desired inhibition effects for both “pressure” and “pinch” stimuli which are considered painful. During control and recovery periods, the algorithm in the feedback loop via wireless communication was turned off before and after the stimulation period. The algorithm also distinguishes innocuous stimuli such as “brush” from the noxious ones, thus there is no stimulation delivered. Note: “*” p < 0.001.

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A batteryless wireless implant architecture has been demonstrated for an esophagus implant to monitor gastroesophageal reflux episodes. The implant, powered wirelessly by a wearable reader worn by the patient, detects both the occurrence of reflux episodes and the pH value of refluxant continuously and with high sampling rates. Both power and data transmission are through the same set of inductive coupling coils. The sensors include an interdigitated finger impedance electrode and an IrOx pH-sensing electrode, both made on biocompatible flexible substrates. The implant harvests radio frequency energy to operate dual- sensor driver and load-modulation circuitry. The external reader can store the data in a memory card or send it to a base station wirelessly, for the cases of multiple-patient monitoring in a hospital or freely- behaving animals experiments. In vivo experiments were conducted with the implant inserted endoscopically through mouth and attached on the esophageal wall. The reflux episodes were created while the sensor data were recorded wirelessly and continuously (Fig. 2). The results show that the impedance sensor responded to the stimulated reflux episodes immediately indicating the ability of detecting short episodes, which current clinical devices may miss. The system detects every episode for both acid and non-acid reflux providing more relevant information for treatment options. The batteryless implant does not require battery replacement allowing longer diagnosis and prognosis periods to monitor drug efficacy in real time and continuously.

Figure 2: In vivo measurement results of the batteryless wireless capsule integrated with both impedance and pH sensors.
Figure 2: In vivo measurement results of the batteryless wireless capsule integrated with both impedance and pH sensors. Episodes are created with solutions of different pH values. The frequency readings of the pH sensors are converted to the actual pH values using the calibration curve for IrOx film. The impedance sensor data is presented with the modulated frequency. At each episode, the impedance sensor reading returns to the base line indicating the occurrence and duration of reflux episodes. The device is capable of detecting not only acidic but also non-acidic events which existing pH-sensor based implant might miss (inside the dashed-line box).

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Similar passive wireless device architectures have also been implemented in other applications such as an endoscopically-implantable wireless gastro-stimulator for gastroparesis management [8], and a wireless bladder volume monitoring implant for urinary incontinence management [9].

In summary, the wireless networking feature and integration of tissue interfaces enable new means for automatic control of disease symptoms and chronic monitoring for prognosis. These capabilities open up significant opportunities to advance medicine, improve human welfare and assist better living.


The research works mentioned were in collaboration with Dr. Yuan-Bo Peng, Dr. Shou-Jiang Tang, Dr. Fred Tibbals, Dr. Stuart Spechler, Dr. Gregory O’Grady, Dr. Smitha Rao, Dr. Leo Cheng, Dr. Ling Gu, Dr. S. K. Mohanty, Dr. Thomas Abell, Dr. Filip To, and Dr. Christopher Lahr.

For Further Reading

[1] M. Mahfouz, G. To, M. Kuhn, “No Strings Attached,” IEEE Microwave Magazine, Vol. 12.No. 7, pp. S34-S48, 2011.

[2] X. Meng, K. D. Browne, S.-M. Huang, C. Mietus, D. K. Cullen, M.-R. Tofighi, A. Rosen, “Dynamic Evaluation of a Digital Wireless Intracranial Pressure Sensor for the Assessment of Traumatic Brain Injury in a Swine Model”, IEEE Transactions on Microwave Theory and Techniques, Vol. 61, No. 1, pp. 316-325, 2013.

[3] A. Farajidavar, C.E. Hagains, Y. B. Peng, J.-C. Chiao, “A Closed Loop Feedback System for Automatic Detection and Inhibition of Mechano-Nociceptive Neural Activity”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 20, No. 4, pp. 478-487, July 2012.

[4] C. Zuo, Y. Wang, C.E. Hagains, A.L. Li, X. Yang, Y. B. Peng and J.-C. Chiao, “A Digital Wireless System for Closed-loop Inhibition of Nociceptive Signals”, Journal of Neural Engineering, Vol. 9, No. 5, 056010, 2012.

[5] H. Cao, V. Landge, U. Tata, Y.-S. Seo, S. Rao, S.-J. Tang, H.F. Tibbals, S. Spechler, and J.-C. Chiao, “An Implantable, Batteryless and Wireless Capsule with Integrated Impedance and pH Sensors for Gastroesophageal Reflux Monitoring”, IEEE Transactions on Biomedical Engineering, Vol. 59, No. 11, pp. 3131-3139, Nov. 2012.

[6] H. Cao, A.-L. Li, C. M. Nguyen, Y.B. Peng, and J.-C. Chiao, “An Integrated Flexible Implantable micro-Probe for Sensing Neurotransmitters”, H. Cao, A.-L. Li, C. M. Nguyen, Y.B. Peng, and J.-C. Chiao, IEEE Sensor Journal, Vo. 12, No. 5, pp. 1618-1624, May 2012.

[7] “An Integrated µLED Optrode for Optogenetic Stimulation and Electrical Recording”, H. Cao, L. Gu, S. K. Mohanty, J.-C. Chiao, IEEE Transactions on Biomedical Engineering, No. 1, Vol. 60, pp. 225-229, 2013.

[8] S. Deb, S.J. Tang, T. Abell, S. Rao, W.-D. Huang, S.D. F. To, C. Lahr, J.-C. Chiao, “An Endoscopic Wireless Gastrostimulator (with video)”, Gastrointestinal Endoscopy, Vol. 75, No. 2, pp. 411-415, 2012.

[9] H. Cao, U. Tata, V. Landge, A.-L. Li, Y.-B. Peng, and J.-C. Chiao, “A Wireless Bladder Volume Monitoring System Using a Flexible Capacitive-based Sensor”, IEEE Topical Conference on Biomedical Wireless Technologies, Networks & Sensing Systems IEEE Radio & Wireless Week, Austin, TX, Jan. 20-23, 2013.

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February 2013 Contributors

Nitish V. ThakorNitish V. Thakor is a Professor of Biomedical Engineering at Johns Hopkins University, Baltimore, USA, as well as the Director of the newly formed institute for neurotechnology, SiNAPSE, at the National University of Singapore. Read more

J. C. ChiaoJ. C. Chiao is a Greene endowed professor and Garrett endowed professor of Electrical Engineering at University of Texas - Arlington... Read more

Xu MengXu Meng (S'08) received a B.E. degree in electronics and telecomm. in 2006 and a M.S. degree in biomedical engineering in 2008 from the Beijing Institute of Technology... Read more

D. Kacy CullenD. Kacy Cullen has B.S. and M.S. degrees in mechanical engineering, in 2002, and a Ph.D. degree in biomedical engineering from the Georgia Institute of Technology in Atlanta, GA... Read more

Mohammad-Reza TofighiMohammad-Reza Tofighi received his B.S.E.E. degree from Sharif University of Technology, Tehran, Iran in 1989, and his M.S.E.E. from Iran University of Science and Technology, Tehran, Iran in 1993. Read more

Arye RosenArye Rosen received a Masters degree in engineering from Johns Hopkins University, a M.Sc. degree in physiology from Jefferson Medical College, and a Ph.D. degree in electrical engineering from Drexel University... Read more

Walker TurnerWalker Turner received B.S. and M.S. degrees in Electrical and Computer Engineering from the University of Florida in 2009 and 2012, respectively. Read more

Dr. Rizwan BashirullahDr. Rizwan Bashirullah received a B.S. in Electrical Engineering from the University of Central Florida and M.S. and Ph.D. degrees in Electrical Engineering from North Carolina State University. Read more

Changzhi LiChangzhi Li received a Ph.D. degree in electrical engineering from the University of Florida in 2009. Read more

Ehsan YavariEhsan Yavari received a B.S.E.E. degree from the Ferdowsi University of Mashhad, Mashhad, Iran, and a M.Sc. degree in electronics from Tarbiat Modares University, Tehran, Iran. Read more

Victor M. LubeckeVictor M. Lubecke received M.S. and Ph.D. degrees in Electrical Engineering from the California Institute of Technology, and a B.S.E.E. degree from the California State Polytechnic Institute, Pomona. Read more

Olga Boric-LubeckeOlga Boric-Lubecke received a M.S. degree from the California Institute of Technology, Pasadena, and a Ph.D. from the University of California at Los Angeles, all in electrical engineering. Read more