A Computationally Efficient Method to Quantify the Biometric Properties of Ventricular Repolarization Irregularities in Healthy and Diseased Human Subjects

Abstract

Repolarization heterogeneity expressed by QT interval prolongation and abnormal temporal dynamics of the QT interval time series is an important factor in relation to coronary heart disease and lethal arrhythmias. Based on our observations, the calculation of window correlation between the mean and variance of features extracted from QT interval time series can reveal natural and disease specific fluctuation patterns. Our algorithm is potentially a sensitive biometric measure to quantify personalized differences and the properties of repolarization heterogeneity, and also a potential biomarker to characterize disease specific QT interval temporal dynamics.