Adaptive Data Processing, Modeling, and Quantification Methods for Analyzing Cardiac Fibrillation

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Heart is a vital organ that beats (i.e. expands and contracts) nonstop to maintain blood circulation to keep us alive. The rhythmic contractions and expansion of the heart transports nutrients and oxygen via blood to all parts of the body to sustain life. When this rhythmic functioning of the heart gets disturbed because of various pathophysiological reasons, arrhythmic contractions result in compromising the normal functioning of the heart. Depending on the origin of these arrhythmic contractions, it may lead to lethal conditions. The most lethal of the arrhythmias is Ventricular Fibrillation (VF) which originates from the lower chambers of the heart (i.e. ventricles). VF can lead to sudden cardiac death (SCD) if no medical attention is provided within minutes of onset. About 300,000 SCDs are reported every year in North America (45,000 in Canada) most of which are VF related. Atrial fibrillation (AF) originating from atria, although not as lethal as VF, can seriously impact quality of life and increases the risk of stroke. Despite research efforts over many decades, there is still a significant knowledge gap in understanding the mechanistic basis of cardiac fibrillation which is preventing effective means to reduce the mortality rates associated with cardiac fibrillation (especially for VF). This strongly motivates the need for developing new engineering methods in understanding mechanisms behind these arrhythmias and translating them to realizable practical solutions to reduce the mortality associated with the arrhythmias. Major bottle necks in decoding the mechanisms behind lethal VF is that SCD occurs within minutes and that in most cases [especially in out-of-the-hospital cardiac arrests (OHCA)] the only immediately available information on the electrical state of the heart is through surface electrocardiograms. In addressing the above knowledge gap, the proposed research program will develop new ways of analyzing and extracting information from multi-channel electrograms and electrocardiograms during arrhythmia and build computer simulation models to decipher the mechanistic insights of cardiac fibrillation. Specifically, the research, in collaboration with Toronto General and St. Michael's Hospitals, will develop advanced data processing and modeling techniques to characterize and regionally locate the sources that initiate and sustain cardiac arrhythmias. The informative clues on these fibrillatory sources will be appropriately translated into electrograms and multi-channel electrocardiogram signal morphologies. These discriminative signal morphologies along with the evolution of the arrhythmia over time will then be used to develop intelligent ablation and defibrillation strategies. The mechanistic knowledge gained through the proposed research program and the developed analysis strategies will significantly augment long-term focused (in-hospital) medical strategies for arrhythmias as well as improve survival rates in OHCA.