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

Janos Palhalmi


Repolarization heterogeneity expressed by QT interval prolongation and abnormal temporal dynamics of the ST interval are important factors in relation to coronary heart disease and lethal arrhythmias. Several statistical measures have been worked out to quantify different patterns that characterize the impaired control of repolarization, but coherence and correlation based methods investigating the relation between the mean and variance of QT and ST interval time series have not been thoroughly investigated. Interval features were extracted from five minutes long lead I ECG recordings of ten healthy human subjects (age: 35-50 years). The coupling between the QT-ST intervals expressed by the overall magnitude squared coherence and Pearson correlation was significant regarding the whole time domain (coherence: 0.85, Pearson correlation coefficient: 0.83, moderated t-test p-value: 0.024). The temporal microstructure of the QT-ST interval coupling was mapped by calculating the magnitude squared coherence and the window correlation between the mean and standard deviation of the QT and ST intervals. In the case of 20 beats window size, the fluctuating pattern of significant coupling and non-significant decoupling between the QT-ST intervals was observed. The overall coherence and correlation were as follows regarding the whole time domain (coherence: 0.60, Pearson correlation coefficient: 0.65, moderated t-test p-value: 0.081). Based on our results, the calculation of window correlation between the mean and standard deviation of QT-ST interval time series can reveal fluctuations in the coupling and decoupling pattern. Our algorithm is potentially a sensitive biometric measure to quantify personalized differences and features of repolarization heterogeneity.