Suppressing the Influence of Ectopic Beats by Applying a Physical Threshold-Based Sample Entropy

Abstract

Sample entropy (SampEn) is widely used for electrocardiogram (ECG) signal analysis to quantify the inherent complexity or regularity of RR interval time series (i.e., heart rate variability (HRV)), with the hypothesis that RR interval time series in pathological conditions output lower SampEn values. However, ectopic beats can significantly influence the entropy values, resulting in difficulty in distinguishing the pathological situation from normal situations. Although a theoretical operation is to exclude the ectopic intervals during HRV analysis, it is not easy to identify all of them in practice, especially for the dynamic ECG signal. Thus, it is important to suppress the influence of ectopic beats on entropy results, i.e., to improve the robustness and stability of entropy measurement for ectopic beats-inserted RR interval time series. In this study, we introduced a physical threshold-based SampEn method, and tested its ability to suppress the influence of ectopic beats for HRV analysis. An experiment on the PhysioNet/MIT RR Interval Databases showed that the SampEn use physical meaning threshold has better performance not only for different data types (normal sinus rhythm (NSR) or congestive heart failure (CHF) recordings), but also for different types of ectopic beat (atrial beats, ventricular beats or both), indicating that using a physical meaning threshold makes SampEn become more consistent and stable.

Assessing Complexity in Physiological Systems through Biomedical Signals Analysis

“…Interestingly, they also indicated how to evaluate the relative consistency in real physiological signals, such as long-term heart rate series, with a computationally efficient algorithm. The consistency of sample entropy for heart rate time series also underlies the work of Zhao et al [ 4 ]. The authors reported that the presence of irregularities in the cardiac contraction (premature or ectopic beats) importantly influenced the sample entropy estimator, even causing a loss of its relative consistency, and address this problem proposing a new way to set the tolerance threshold.…”

Influence of Ectopic Beats on Heart Rate Variability Analysis

“…Researchers developed various methods for r value selection [ 29 , 30 ], but it seems that different methods perform well only under certain circumstances. The concept of the physical threshold was partly from a study of AF detection use SampEn [ 31 ], and our previous study certified that this physical threshold could perform well on NSR and CHF RR interval time series with ectopic beats [ 25 ] since it could avoid invalid entropy values in each RR segment, and a more stable specific r value could be determined by the sampling the resolution of physiological signals. The reason could be explained by the entropy calculation process.…”

“…The SampEn has been proved more stable and consistent statistically than ApEn since SampEn excludes the self-matching in its calculation [ 21 , 22 ], and there are also various improvement methods based on ApEn and SampEn [ 23 , 24 ]. Ectopic beats can contaminate the entropy calculations as well [ 17 , 25 ]. The quantitative relationship between the burden of ectopic beats and HRV indices, including entropy measures, has not yet been investigated in depth.…”

Development of a Neurodegenerative Disease Gait Classification Algorithm Using Multiscale Sample Entropy and Machine Learning Classifiers

“…In addition, entropy method is quite popular in electroencephalogram (EEG) signal [ 24 , 25 , 26 , 27 ], electrocardiogram (ECG) signal [ 28 , 29 ], and electromyography (EMG) signal [ 30 , 31 ]. Mizuno et al and Labate et al used MSE to analyze the complexity of signaling in patients with Alzheimer’s disease [ 27 , 32 ].…”

“…In addition, Platiša et al used MSE to measure the complexities of the cardiorespiratory system over the cardiac interval [ 21 ] and Roldan et al used MSE to analyze the f -waves may provide early prediction of atrial fibrillation recurrence after electrical cardioversion in ECG signals [ 28 ]. Zhao et al applied a threshold-based sample entropy to suppress the influence of ectopic beats for heart rate variability analysis [ 29 ]. Regarding applying the entropy theorem in EMG analysis, Trybek et al and Qin et al extracted the MSE features to evaluate the surface electromyography (sEMG) signals [ 30 , 31 ].…”