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

Objective: 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. Methods: 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. Results: Encouraging evaluation results are achieved on electrocardiograms from public PTB database and hospital patients. Significant correlations are found between the CDG morphology and ischemia. Conclusion: 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. Significance: 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.