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 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.
Highlights
Atrial fibrillation (AF) is a typical arrythmia, defined as a tachyarrhythmia characterized by predominantly uncoordinated atrial activation with consequent deterioration of atrial mechanicalEntropy 2018, 20, 904; doi:10.3390/e20120904 www.mdpi.com/journal/entropyEntropy 2018, 20, 904 function [1,2]
Compared with other three entropy measures, EntropyAF resulted in obviously larger values of Sp of 87.91% and Acc of 92.85% for classifying atrial fibrillation (AF) and N rhythms, obviously larger values of Sp of 86.01% and Acc of 91.60% for classifying AF and non-AF rhythms
coefficient of sample entropy (COSEn) had the second-best performance on the classification of AF and non-AF rhythms, outputting an Acc of 78.63%, followed by an Acc of 65.08% for FuzzyMEn and an Acc of 59.91% for sample entropy (SampEn)
Summary
Atrial fibrillation (AF) is a typical arrythmia, defined as a tachyarrhythmia characterized by predominantly uncoordinated atrial activation with consequent deterioration of atrial mechanicalEntropy 2018, 20, 904; doi:10.3390/e20120904 www.mdpi.com/journal/entropyEntropy 2018, 20, 904 function [1,2]. Atrial fibrillation (AF) is a typical arrythmia, defined as a tachyarrhythmia characterized by predominantly uncoordinated atrial activation with consequent deterioration of atrial mechanical. AF is associated with significant mortality and morbidity, resulting in that more than 12 million Europeans and North Americans suffer from AF [3,4]. Current diagnose for AF is under-detected and under-diagnosed due to the asymptomatic characteristic of AF episode, which can elude clinical detection [6]. And accurate detection of AF for real-time monitoring and feedback is challenging [7,8]. Twelve-lead 24-h Holter is a common method in clinic for AF detection. This technique is effective to diagnose patients suffering from persistent AF but may miss many cases of paroxysmal
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