| Sudden Cardiac Death (SCD), a death occurring due to abrupt loss of heart function is a leading cause of death in industrialized countries. As the event can occur unexpectedly without prior symptoms, it is has become a major clinical challenge to try to predict these events. One approach is to use electrocardiograph (ECG) information, because it is a non-invasive technique for measuring the heart's electrical activity. There are many options available for analyzing ECG signals. One approach, the T wave alternans (TWA) approach, has been successfully used to analyze risk levels of cardiac fibrillations, one of the predecessors of SCD.;The objective of this work is to examine the viability of using the TWA approach to predict sudden cardiac death. Specifically, the Modified Moving Average algorithm (MMA) for T wave alternans, a time domain based analysis has been used, as it is the best applicable method, with respect to modeling of the experiments and data collection. The algorithm has been successfully implemented in MATLAB, as verified by simulated ECG signals.;The application to the prediction of SCD has been demonstrated using data collected from pigs, modeled to mimic coronary artery disease, the most common cause of sudden cardiac death. Twenty-one pigs were analyzed at two different times on two different days to determine if there was significant difference between pigs in their TWA amplitude or frequency of occurrence. As yet, no statistical significance has been shown.;This work is a first step toward understanding the application of TWA to sudden cardiac death prediction. Future work can include more analysis of the swine data and refinements of the TWA algorithm. |
