A comparison of entropy approaches for AF discrimination. (5th July 2018)

• Record Type: Journal Article
• Authors: Liu, Chengyu; Oster, Julien; Reinertsen, Erik; Li, Qiao; Zhao, Lina; Nemati, Shamim; Clifford, Gari D
• Is Part Of: Physiological measurement. Volume 39:Number 7(2018:Jul.)
• Publication Date: 2018-07-05
• Subjects: atrial fibrillation -- ECG -- entropy -- normalized fuzzy entropy; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/aacc48
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Objective : This study focuses on the comparison of single entropy measures for ventricular response analysis-based AF detection. Approach : To enhance the performance of entropy-based AF detectors, we developed a normalized fuzzy entropy, , a novel metric that (1) uses a fuzzy function to determine vector similarity, (2) replaces probability estimation with density estimation for entropy approximation, (3) utilizes a flexible distance threshold parameter, and (4) adjusts for heart rate by subtracting the natural log value of the mean RR interval. An AF detector based on was trained using the MIT-BIH atrial fibrillation (AF) database, and tested on the MIT-BIH normal sinus rhythm (NSR) and MIT-BIH arrhythmia databases. The -based AF detector was compared to AF detectors based on three other entropy measures: sample entropy ( ), fuzzy measure entropy ( ) and coefficient of sample entropy ( ), over three standard window sizes. Main results : To classify AF and non-AF rhythms, achieved the highest area under receiver operating characteristic curve ( AUC ) values of 92.72%, 95.27% and 96.76% for 12-, 30- and 60-beat window lengths respectively. This was higher than the performance of the next best technique, , over all windows sizes, which provided respective AUCs of 91.12%, 91.86% and 90.55%. and resulted in lower AUCs (below 90%) over all window sizes. also provided superior performance for all other tested statistics, including the Youden index, sensitivity, specificity, accuracy, positive predictivity and negative predictivity. In conclusion, we show that can be used to accurately identify AF from RR interval time series. Furthermore, longer window lengths (up to one minute) increase the performance of all entropy-based AF detectors under evaluation except the -based method. Significance : Our results demonstrate that the new developed normalized fuzzy entropy is an accurate measure for detecting AF.