Speaker: Ozan Biçen, Sabancı University
Title: Computational Prototypes and Biomarkers for Mobile Health Sensing
Date/Time: October 9, 2019 / 13.40-14.30
Place: FENS G032
Abstract: ost-effective mobile health sensing technologies are essential to move toward affordable personalized healthcare solutions. While sensing of body functions (physiology) from non-invasive inexpensive hardware is convenient and facilitates point-of-care monitoring of various health parameters, such measurements are susceptible to many sources of noise. The motion and instrumentation noise may be so significant that the estimated health parameters may not be accurate. In addition, physiological and biochemical signals are highly variable from person to person (normal variability), and, thus, it is not obvious from a measurement which components are “signal” and which are “noise”. An important question surrounding the distinction between signal and noise that must be addressed is how can we determine the “reliability” of the acquired signals and computed parameters? To this end, mathematical models and signal processing algorithms need to be tailored and customized based on the individual variations and time-varying dynamics of the sensed phenomena.
In this talk, I will discuss non-invasive cardiovascular health monitoring and the challenges for the efficacy of wearable and portable cardiovascular sensing systems in uncontrolled settings. Effective home monitoring systems can enable proactive treatment of heart disease that improves the prognosis while reducing the monetary and lifestyle burdens. However, non-invasive cardiac measurements in unsupervised settings are error-prone. I will show in which ways the tailored computational prototypes and biomarkers are particularly important for determining system behavior when non-linear and stochastic phenomena co-exist and interfere with each other. Statistical and periodic properties (characteristics) of the physiological signals carry utmost importance for the reliable detection and estimation of health parameters in mobile health sensing technologies. I will describe the devised methodology for the detection of the noisy measurements to reduce the error in the estimated health parameters. In addition, the analysis of distortion in the signals is foundational to the performance of many kinds of sensor systems, including the sensing of biochemical signals. I will discuss how model-based signal processing can improve the detection accuracy and provide performance assessment for lab-on-a-chip systems and biosensors.
BIO: Ozan Bicen received his B.Sc. degree in Electrical and Electronics Engineering from Middle East Technical University, Ankara, Turkey, in 2010. He received his M.Sc. in Electrical and Electronics Engineering from Koc University, Istanbul, Turkey in 2012. He obtained his Ph.D. in Electrical and Computer Engineering at the Georgia Institute of Technology, Atlanta, Georgia in 2016. He was a postdoctoral researcher in the School of Electrical and Computer Engineering at the Georgia Institute of Technology prior to joining Faculty of Engineering and Natural Sciences at the Sabanci University, Istanbul, Turkey in 2019. His research has been at the intersection of signal processing, mathematical modeling, and statistical analysis in relation to physical, biological, and engineered systems. Specifically, he has been working on: 1) algorithm development for non-invasive sensing of physiological signals; and 2) system-theoretic modeling and performance analysis of microfluidics-based lab-on-a-chip systems. His current research interests include statistical signal processing, physiological modeling, mobile health sensing, and computational biomarkers.
Contact: Ozan Biçen