A New Physically Meaningful Threshold of Sample Entropy for Detecting Cardiovascular Diseases

Xiong, Jing-Jing; Liang, Xun; Zhu, Tingting; Zhao, Lina; Li, Jianqing; Liu, Chengyu

doi:10.3390/e21090830

Cited by 8 publications

(19 citation statements)

References 36 publications

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“…Thus, we tested the effects of different

values of

etc., and found that

provided the best discrimination between the CHF and NSR groups. In this study, we used the previously proposed fixed tolerance

method with

[ 26 ] with physical meaning to analyze the RR interval time series with ectopic beats, to explore if the new

method has better performance for ectopic time series. Forty-five NSR and 24 CHF recordings were enrolled in this study, all of which had an appreciable number of ectopic beats, including atrial and ventricular beats.…”

Section: Discussionmentioning

confidence: 99%

“…The opposite entropy change trend brings difficulty to defining a unified threshold

to distinguish CHF patients from NSR subjects in heart rate variability (HRV) analysis. To solve this problem, we proposed a physical threshold-based SampEn method to discriminate the opposite entropy change trend in the task of classifying CHF and NSR subjects [ 26 ], where the physical threshold-based SampEn was demonstrated to have a better stability than the traditional SampEn.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Xiong

et al. 2020

Entropy

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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.

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“…Thus, we tested the effects of different

values of

etc., and found that

provided the best discrimination between the CHF and NSR groups. In this study, we used the previously proposed fixed tolerance

method with

[ 26 ] with physical meaning to analyze the RR interval time series with ectopic beats, to explore if the new

Section: Discussionmentioning

confidence: 99%

“…The opposite entropy change trend brings difficulty to defining a unified threshold

Section: Introductionmentioning

confidence: 99%

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

Zhao

Xiong

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…The inverted entropy results make it hard to establish a unified standard to detect CHF subjects with a constant r value. To avoid such inconsistency, we proposed a physical threshold as multiple of sampling period, and proved that it is more adaptive to CHF detection than the traditional threshold [ 29 ]. Since the signals were sampled at 128 Hz, we regarded sampling period as 8 ms, and set threshold r as 1.5 times the sampling period, which equals to 12 ms.…”

Section: Methodsmentioning

confidence: 99%

“…In previous study [ 29 ], we used physical threshold-based SampEn to distinguish CHF subjects from NSR subjects. As the results have shown, the average SampEn values of CHF group are significantly lower than those of NSR group when using m = 1, 2 and N = 300, 1000, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Improving Accuracy of Heart Failure Detection Using Data Refinement

Xiong

Liang

Zhao

et al. 2020

Entropy

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Due to the wide inter- and intra-individual variability, short-term heart rate variability (HRV) analysis (usually 5 min) might lead to inaccuracy in detecting heart failure. Therefore, RR interval segmentation, which can reflect the individual heart condition, has been a key research challenge for accurate detection of heart failure. Previous studies mainly focus on analyzing the entire 24-h ECG recordings from all individuals in the database which often led to poor detection rate. In this study, we propose a set of data refinement procedures, which can automatically extract heart failure segments and yield better detection of heart failure. The procedures roughly contain three steps: (1) select fast heart rate sequences, (2) apply dynamic time warping (DTW) measure to filter out dissimilar segments, and (3) pick out individuals with large numbers of segments preserved. A physical threshold-based Sample Entropy (SampEn) was applied to distinguish congestive heart failure (CHF) subjects from normal sinus rhythm (NSR) ones, and results using the traditional threshold were also discussed. Experiment on the PhysioNet/MIT RR Interval Databases showed that in SampEn analysis (embedding dimension m = 1, tolerance threshold r = 12 ms and time series length N = 300), the accuracy value after data refinement has increased to 90.46% from 75.07%. Meanwhile, for the proposed procedures, the area under receiver operating characteristic curve (AUC) value has reached 95.73%, which outperforms the original method (i.e., without applying the proposed data refinement procedures) with AUC of 76.83%. The results have shown that our proposed data refinement procedures can significantly improve the accuracy in heart failure detection.

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Decreased sample entropy during sleep-to-wake transition in sleep apnea patients

et al. 2021

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Objective. This study aimed to prove that there is a sudden change in the human physiology system when switching from one sleep stage to another and physical threshold-based sample entropy (SampEn) is able to capture this transition in an RR interval time series from patients with disorders such as sleep apnea. Approach. Physical threshold-based SampEn was used to analyze different sleep-stage RR segments from sleep apnea subjects in the St. Vincents University Hospital/University College Dublin Sleep Apnea Database, and SampEn differences were compared between two consecutive sleep stages. Additionally, other standard heart rate variability (HRV) measures were also analyzed to make comparisons. Main results. The findings suggested that the sleep-to-wake transitions presented a SampEn decrease significantly larger than intra-sleep ones (P < 0.01), which outperformed other standard HRV measures. Moreover, significant entropy differences between sleep and subsequent wakefulness appeared when the previous sleep stage was either S1 (P < 0.05), S2 (P < 0.01) or S4 (P < 0.05). Significance. The results demonstrated that physical threshold-based SampEn has the capability of depicting physiological changes in the cardiovascular system during the sleep-to-wake transition in sleep apnea patients and it is more reliable than the other analyzed HRV measures. This noninvasive HRV measure is a potential tool for further evaluation of sleep physiological time series.

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A New Physically Meaningful Threshold of Sample Entropy for Detecting Cardiovascular Diseases

Cited by 8 publications

References 36 publications

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

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

Improving Accuracy of Heart Failure Detection Using Data Refinement

Decreased sample entropy during sleep-to-wake transition in sleep apnea patients

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