Ki Chon, PhD

Expertise: non-linear biosignal analysis, non-linear dynamical analysis, signal & image processing, heart rate variability signal analysis, linear and non-linear time-varying analysis, smartphone application, time-frequency analysis

Ki H. Chon received the B.S. degree in electrical engineering from the University of Connecticut, Storrs; the M.S. degree in biomedical engineering from the University of Iowa, Iowa City; and the M.S. degree in electrical engineering and the Ph.D. degree in biomedical engineering from the University of Southern California, Los Angeles. He spent three years as an NIH Post-Doctoral fellow at the Harvard-MIT Division of Health Science and Technology. He is currently the John and Donna Krenicki Endowed Chair Professor and Head of Biomedical Engineering at University of Connecticut, Storrs, CT.

He has published more than 114 peer-reviewed journal articles to date and has 6 U.S. patents granted. His patent on real-time detection of atrial fibrillation algorithm has been licensed to a Holter company and the Holter is currently on the market.

He was an Associate Editor of the IEEE Transactions on Biomedical Engineering from 2007-2013. He has chaired many international conferences including his role as the Program Co-Chair for the IEEE EMBS conference in NYC in 2006, and as the Conference Chair for the 6th International Workshop on Biosignal Interpretation in New Haven, CT in 2009. He is a fellow of the American Institute of Medical and Biological Engineering and of the International Academy of Medical and Biological Engineering.

Research Interests: Medical instrumentation, biomedical signal processing, and identification and modeling of physiological systems.

Research Summary: Research in my laboratory involves medical instrumentation, biosignal processing, modeling, simulation and development of novel algorithms to understand dynamic processes and extract distinct features of physiological systems. Currently, there are six research projects ongoing in my laboratory:

1. Evaluation of the effects of oxygen toxicity and hyperbaric environments on the autonomic nervous system: The goal is to develop noninvasive approaches for early detection of and differentiation between fatal and non-fatal decompression sickness (DCS). Both swine and human experiments are being conducted to test the robustness of our algorithm for early detection and prediction of DCS.
2. Real-time detection of atrial fibrillation, atrial flutter and atrial tachycardia from surface ECG: The goal is to develop real-time algorithms for accurate detection of atrial fibrillation, flutter and tachycardia that are especially applicable for Holter monitoring devices.
3. Spatio-temporal analysis of renal autoregulation: The goal is to understand how nephrons synchronize to autoregulate renal blood flow using laser speckle imaging techniques.
4. Noninvasive assessment of diabetic cardiovascular autonomic neuropathy (DCAN) from surface ECG or pulse oximeter: The goal is to develop noninvasive approaches for early detection of DCAN. Diabetic and control mice are used to collect ECG data and validation of computational data analysis results is measured against Western blot and immunohistochemistry.
5. Vital sign monitoring from optical recordings with a mobile phone: The goal is to utilize a mobile phone video camera to extract vital sign and physiological parameters, which may include heart rate, oxygen saturation, respiratory rate, atrial fibrillation detection, blood loss detection, and the dynamics of the autonomic nervous system.
6. Wearable devices for vital sign monitoring: The goal is to develop wearable devices (e.g., chest strap, wearable shirt and watches) and new sensors (e.g., dry ECG, skin conductance and EMG electrodes) to measure vital sign and physiological parameters for both dry and water immersion conditions.

Honors and Awards:

• Fellow, IEEE Fellow, National Academy of Inventors
• Fellow, International Academy of Medical and Biological Engineering (IAMBE)
• Fellow, American Institute for Medical and Biological Engineering (AIMBE)
• Fellow, Asia-Pacific Artificial Intelligence Association
• Program Co-Chair, 28th International Conference of the IEEE EMBS, 2006, NYC.
• Krenicki Endowed Chair Professor at UConn