Cuffless Blood Pressure Monitoring using Bio-Impedance
Continuous and robust monitoring of physiological signals with wearable devices provides new opportunities for improving the care and outcomes for people with or at risk of a broad range of adverse health events. One challenge has been that many devices are obtrusive, cumbersome, not very convenient to wear, and unsuitable for ambulatory care. Limited reliability and robustness of these sensing paradigms have also been among contributing factors that prohibit the adoption of wearable devices into the FDA-regulatory space and medical applications. Another challenge to the current method of measuring certain hemodynamic parameters such as blood pressure (BP), is that cuff-based sensors are only capable of providing infrequent measurements and are uncomfortable. Elevated BP is a critically important risk factor for various cardiovascular disorders (i.e., heart attack, stroke, heart failure), kidney diseases, vision loss, and sexual dysfunction. In this talk, we discuss our techniques that directly address these unmet needs for a device that can unobtrusively, accurately, and continuously measure BP. We seek to develop and test a novel transformative solution which addresses concerns of wearability and robust sensing, and enables new sensing paradigms that can be deployed for field-based, mobile, or ambulatory care leveraging bio-impedance. We will discuss a number of sensing, circuit and signal processing paradigms that capture physiological observations including bio-impedance. Our primary focus remains estimating blood pressure from a wrist-worn device with a watch form factor with high degrees of precision. We will discuss several methodologies for noise rejection that improve the robustness of signal acquisition. We will also discuss machine learning techniques that converts our bio-impedance observations to blood pressure. We will offer concluding remarks on the trends of wearable computing technology development and potential future directions.
Roozbeh Jafari (http://jafari.tamu.edu) is a professor of Biomedical Engineering, Computer Science and Engineering and Electrical and Computer Engineering at Texas A&M University. He received his Ph.D. in Computer Science from UCLA and completed a postdoctoral fellowship at UC-Berkeley. His research interest lies in the area of wearable computer design and signal processing. He has raised more than $87M for research with $28M directed towards his lab. His research has been funded by the NSF, NIH, DoD (TATRC, DTRA, DIU), AFRL, AFOSR, DARPA, SRC and industry (Texas Instruments, Tektronix, Samsung & Telecom Italia). He has published over 180 papers in refereed journals and conferences. He has served as the general chair and technical program committee chair for several flagship conferences in the area of Wearable Computers. Dr. Jafari is the recipient of the NSF CAREER award (2012), IEEE Real-Time & Embedded Technology & Applications Symposium best paper award (2011), Andrew P. Sage best transactions paper award (2014), ACM Transactions on Embedded Computing Systems best paper award (2019), and the outstanding engineering contribution award from the College of Engineering at Texas A&M (2019). He was named Texas A&M Presidential Impact Fellow (2019). He is an associate editor for the IEEE Transactions on Biomedical Circuits and Systems, IEEE Sensors Journal, IEEE Internet of Things Journal, IEEE Journal of Biomedical and Health Informatics and ACM Transactions on Computing for Healthcare. He serves on scientific panels for funding agencies frequently and is presently serving as a standing member of the NIH Biomedical Computing and Health Informatics study section and the chair of the Clinical Informatics and Digital Health study section.