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Open AccessArticle

A New Entropy-Based Atrial Fibrillation Detection Method for Scanning Wearable ECG Recordings

by 1,2,†, 2,*,†, 1,*, 3, 2 and 2
1
School of Control Science and Engineering, Shandong University, Jinan 250061, China
2
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
3
Department of Cardiovascular Medicine, First Affiliated Hospital of Nanjing Medical University, Nanjing 210036, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this paper.
Entropy 2018, 20(12), 904; https://doi.org/10.3390/e20120904
Received: 1 November 2018 / Revised: 18 November 2018 / Accepted: 23 November 2018 / Published: 26 November 2018
Entropy-based atrial fibrillation (AF) detectors have been applied for short-term electrocardiogram (ECG) analysis. However, existing methods suffer from several limitations. To enhance the performance of entropy-based AF detectors, we have developed a new entropy measure, named EntropyAF, which includes the following improvements: (1) use of a ranged function rather than the Chebyshev function to define vector distance, (2) use of a fuzzy function to determine vector similarity, (3) replacement of the probability estimation with density estimation for entropy calculation, (4) use of a flexible distance threshold parameter, and (5) use of adjusted entropy results for the heart rate effect. EntropyAF was trained using the MIT-BIH Atrial Fibrillation (AF) database, and tested on the clinical wearable long-term AF recordings. Three previous entropy-based AF detectors were used for comparison: sample entropy (SampEn), fuzzy measure entropy (FuzzyMEn) and coefficient of sample entropy (COSEn). For classifying AF and non-AF rhythms in the MIT-BIH AF database, EntropyAF achieved the highest area under receiver operating characteristic curve (AUC) values of 98.15% when using a 30-beat time window, which was higher than COSEn with AUC of 91.86%. SampEn and FuzzyMEn resulted in much lower AUCs of 74.68% and 79.24% respectively. For classifying AF and non-AF rhythms in the clinical wearable AF database, EntropyAF also generated the largest values of Youden index (77.94%), sensitivity (92.77%), specificity (85.17%), accuracy (87.10%), positive predictivity (68.09%) and negative predictivity (97.18%). COSEn had the second-best accuracy of 78.63%, followed by an accuracy of 65.08% in FuzzyMEn and an accuracy of 59.91% in SampEn. The new proposed EntropyAF also generated highest classification accuracy when using a 12-beat time window. In addition, the results from time cost analysis verified the efficiency of the new EntropyAF. This study showed the better discrimination ability for identifying AF when using EntropyAF method, indicating that it would be useful for the practical clinical wearable AF scanning. View Full-Text
Keywords: atrial fibrillation (AF); sample entropy (SampEn); fuzzy measure entropy (FuzzyMEn); coefficient of sample entropy (COSEn); wearable ECG; RR time series; cardiac rhythm atrial fibrillation (AF); sample entropy (SampEn); fuzzy measure entropy (FuzzyMEn); coefficient of sample entropy (COSEn); wearable ECG; RR time series; cardiac rhythm
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MDPI and ACS Style

Zhao, L.; Liu, C.; Wei, S.; Shen, Q.; Zhou, F.; Li, J. A New Entropy-Based Atrial Fibrillation Detection Method for Scanning Wearable ECG Recordings. Entropy 2018, 20, 904.

AMA Style

Zhao L, Liu C, Wei S, Shen Q, Zhou F, Li J. A New Entropy-Based Atrial Fibrillation Detection Method for Scanning Wearable ECG Recordings. Entropy. 2018; 20(12):904.

Chicago/Turabian Style

Zhao, Lina; Liu, Chengyu; Wei, Shoushui; Shen, Qin; Zhou, Fan; Li, Jianqing. 2018. "A New Entropy-Based Atrial Fibrillation Detection Method for Scanning Wearable ECG Recordings" Entropy 20, no. 12: 904.

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