Bivariate Entropy Analysis of Electrocardiographic RR-QT Time Series.

Bo Shi,Mohammod Abdul Motin,Peng Li,Xinpei Wang,Chandan Karmakar

doi:10.3390/e22121439

Bo Shi, Mohammod Abdul Motin + Show 3 more

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https://doi.org/10.3390/e22121439

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Abstract

QT interval variability (QTV) and heart rate variability (HRV) are both accepted biomarkers for cardiovascular events. QTV characterizes the variations in ventricular depolarization and repolarization. It is a predominant element of HRV. However, QTV is also believed to accept direct inputs from upstream control system. How QTV varies along with HRV is yet to be elucidated. We studied the dynamic relationship of QTV and HRV during different physiological conditions from resting, to cycling, and to recovering. We applied several entropy-based measures to examine their bivariate relationships, including cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), cross conditional entropy (XCE), and joint distribution entropy (JDistEn). Results showed no statistically significant differences in XSampEn, XFuzzyEn, and XCE across different physiological states. Interestingly, JDistEn demonstrated significant decreases during cycling as compared with that during the resting state. Besides, JDistEn also showed a progressively recovering trend from cycling to the first 3 min during recovering, and further to the second 3 min during recovering. It appeared to be fully recovered to its level in the resting state during the second 3 min during the recovering phase. The results suggest that there is certain nonlinear temporal relationship between QTV and HRV, and that the JDistEn could help unravel this nuanced property.

Highlights

Representing ventricular repolarization lability, the beat-to-beat QT interval variability (QTV)in the electrocardiogram (ECG) is an accepted biomarker for the risk of ventricular arrhythmias and cardiac mortality [1,2,3,4]
As QT interval readily adapts to the change in heart rate (HR), the QTV is strongly modulated by the heart rate variability (HRV)
Different from prior studies that had focused on formulating the relationship with an explicit equation, such as a polynomial function, we studied the cross-predictability or synchrony between QT and RR interval time series assessed by four bivariate entropy measures that characterized how the two processes vary along with each other

Summary

Introduction

Representing ventricular repolarization lability, the beat-to-beat QT interval variability (QTV)in the electrocardiogram (ECG) is an accepted biomarker for the risk of ventricular arrhythmias and cardiac mortality [1,2,3,4]. As QT interval readily adapts to the change in heart rate (HR), the QTV is strongly modulated by the HRV. Instead of seeking a generalized equation between RR and QT across multiple subjects, prior studies have investigated individualized transfer function to characterize the subject-specific RR–QT dynamical relationship [14,15]. They are so far limited to the use of linear functions such as the auto regressive moving average model. As a result of the competition of spontaneity and adaptability [16,17], it is widely accepted that the cardiac activity is nonlinear in nature

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