Sample entropy of electrocardiographic RR and QT time-series data during rest and exercise

Abstract

Sample entropy (SampEn) is a measure of the complexity of data. Few studies have compared the SampEn of electrocardiographic cardiac interval (RR) data (SampEn-RR) during differing physiological states, and none have examined SampEn for the corresponding QT interval (SampEn-QT). The aim of this study was to quantify SampEn-RR and SampEn-QT during rest and for a range of exercise workloads. Specific objectives were to assess both the utility of SampEn for discriminating between physiological states and the relationship of SampEn-RR with traditional measures of heart rate variability (HRV). Twelve males of similar age, mass and aerobic fitness participated. A three-lead ECG was recorded continuously during pre-exercise, progressive bicycle exercise and recovery, and beat-to-beat RR and QT intervals were quantified for sinus beats. SampEn and HRV were calculated within consecutive 1 min periods throughout. Consistent estimation of SampEn-RR and SampEn-QT was possible with an appropriate choice of SampEn parameters. SampEn-RR was sensitive to differing physiological conditions, but its discriminating ability was poorer than that of linear HRV indices. SampEn-RR was also negatively correlated with normalized LF and LF/HF parameters. We interpret changes in SampEn for RR and QT data in terms of the altered autonomic nervous system (ANS) control of either the atrial or the ventricular myocardium (or both) during discrete physiological states. We speculate that greater complexity in QT data might be explained by a direct ANS influence on the ventricular myocardium.