Computational Methods for Physiological Data

Dr. Syed is from MIT
Large volumes of physiological data are now collected as part of routine patient care and in clinical trials. These datasets present an exciting opportunity to advance patient care by capturing prognostic phenomena associated with specific medical conditions, and by providing fresh insights into disease dynamics over long time scales.

In this talk, I will describe how progress in medicine can be accelerated through the use of sophisticated computational methods for the structured analysis of large multi-patient, multi-signal datasets. In particular, I will describe two new approaches physiological symbolic analysis and morphologic variability that we have recently proposed. I will also present some results we have obtained by applying these methods to large datasets (approximately 1 billion heart beats) of cardiovascular signals. Among other results, we show that morphological variability can identify patients at a significantly increased risk of death and heart attack, even after adjusting for other clinical predictors.

This work is being carried out in collaboration with the Brigham and Women’s Hospital in Boston.

Zeeshan Syed is a Ph.D. candidate in the MIT Department of Electrical Engineering and Computer Science, and the Harvard-MIT Division of Health Sciences and Technology. His doctoral research focuses on the development of novel computational methods to analyze large amounts of physiological data, and to predict adverse outcomes (e.g., death and heart attacks) in patients with cardiovascular disease. Zeeshan previously received his S.B. and M.Eng. from MIT in Electrical Engineering and Computer Science. He has research interests spanning data mining, machine learning, signal processing, applied algorithms, and clinical medicine.