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

Lina Zhao; Chengyu Liu; Shoushui Wei; Qin Shen; Fan Zhou; Jianqing Li

doi:10.3390/e20120904

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

Entropy (Basel). 2018 Nov 26;20(12):904. doi: 10.3390/e20120904.

Authors

Lina Zhao^{1

2}, Chengyu Liu², Shoushui Wei¹, Qin Shen³, Fan Zhou², Jianqing Li²

Affiliations

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.

Abstract

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 Entropy_AF, 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. Entropy_AF 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, Entropy_AF 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, Entropy_AF 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 Entropy_AF 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 Entropy_AF. This study showed the better discrimination ability for identifying AF when using Entropy_AF method, indicating that it would be useful for the practical clinical wearable AF scanning.

Keywords: RR time series; atrial fibrillation (AF); cardiac rhythm; coefficient of sample entropy (COSEn); fuzzy measure entropy (FuzzyMEn); sample entropy (SampEn); wearable ECG.

Abstract

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