Next Article in Journal
Higher-Order Hamiltonian for Circuits with (α,β) Elements
Next Article in Special Issue
Zipf’s Law of Vasovagal Heart Rate Variability Sequences
Previous Article in Journal
Utilizing Amari-Alpha Divergence to Stabilize the Training of Generative Adversarial Networks
Previous Article in Special Issue
Entropy in Heart Rate Dynamics Reflects How HRV-Biofeedback Training Improves Neurovisceral Complexity during Stress-Cognition Interactions
Open AccessArticle

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

1
The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
2
School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
*
Authors to whom correspondence should be addressed.
Entropy 2020, 22(4), 411; https://doi.org/10.3390/e22040411
Received: 17 February 2020 / Revised: 31 March 2020 / Accepted: 1 April 2020 / Published: 4 April 2020
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. View Full-Text
Keywords: sample entropy; heart rate variability; ECG; ectopic beat sample entropy; heart rate variability; ECG; ectopic beat
Show Figures

Figure 1

MDPI and ACS Style

Zhao, L.; Li, J.; Xiong, J.; Liang, X.; Liu, C. Suppressing the Influence of Ectopic Beats by Applying a Physical Threshold-Based Sample Entropy. Entropy 2020, 22, 411.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop