Description:

Title:

Machine Learning for Cardiac Arrhythmia Monitoring and Severe Event Prediction

Abstract:

Abnormalities of cardiac rhythms are correlated with significant morbidity. Due to the time-sensitive nature of cardiac events, it is of utmost importance to ensure that medical intervention is provided in a timely manner, which could benefit greatly from a cardiac arrhythmia monitoring system that can detect and preferably also predict the abnormal cardiac events. In recent years, with the development of medical monitoring devices, vast amounts of physiological signal data have been collected and become available for analysis. Machine learning-based methods have found important applications in detection and prediction of cardiac events. In this talk, I will provide a survey of existing machine learning methods, and also present a novel algorithm based on deterministic probabilistic finite-state automata.