Deterministic Learning-Based Methodology for Detecting Abnormal Dynamics of Cardiac Repolarization During Ischemia.

Abstract

This study concentrates on subtle electrocardiogram (ECG) spatiotemporal characteristics in the repolarization phase, and describes a deterministic learning-based methodology for the detection of abnormal cardiac dynamics induced by ischemia. ST-T complex of the surface 12-lead ECG signals are identified and extracted. Cardiac dynamics underlying ST-T complex signals is captured using deterministic learning algorithm. This kind of dynamics information represents the beat-to-beat temporal change of electrophysiological modifications in ventricular repolarization, which is shown to be sensitive to the variance during myocardial ischemia. Cardiodynamicsgram (CDG) is proposed as the three-dimensional graphic representation of cardiac dynamics information. Encouraging evaluation results are achieved on electrocardiograms from public PTB database and hospital patients. Significant correlations are found between the CDG morphology and ischemia. Anormal dynamics of cardiac repolarization during ischemia can be detected using a deterministic learning-based methodology. The extracted cardiac dynamics information within routine ECG is expected to provide early detection for latent ischemia before obvious pathological changes are present in ECG. The proposed techniques can be considered as a complementary tool to the generally accepted ECG method for detection of abnormal dynamics in cardiac repolarization, which are important for identifying patients at risk of myocardial ischemia.