Chengyu Liu1, Julien Oster, Erik Reinertsen, Qiao Li, Lina Zhao, Shamim Nemati, Gari D Clifford. 1. The State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, People's Republic of China. Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America.
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, [Formula: see text], 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 [Formula: see text] 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 [Formula: see text]-based AF detector was compared to AF detectors based on three other entropy measures: sample entropy ([Formula: see text]), fuzzy measure entropy ([Formula: see text]) and coefficient of sample entropy ([Formula: see text]), over three standard window sizes. MAIN RESULTS: To classify AF and non-AF rhythms, [Formula: see text] 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, [Formula: see text], over all windows sizes, which provided respective AUCs of 91.12%, 91.86% and 90.55%. [Formula: see text] and [Formula: see text] resulted in lower AUCs (below 90%) over all window sizes. [Formula: see text] 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 [Formula: see text] 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 [Formula: see text]-based method. SIGNIFICANCE: Our results demonstrate that the new developed normalized fuzzy entropy is an accurate measure for detecting AF.
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, [Formula: see text], 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 [Formula: see text] 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 [Formula: see text]-based AF detector was compared to AF detectors based on three other entropy measures: sample entropy ([Formula: see text]), fuzzy measure entropy ([Formula: see text]) and coefficient of sample entropy ([Formula: see text]), over three standard window sizes. MAIN RESULTS: To classify AF and non-AF rhythms, [Formula: see text] 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, [Formula: see text], over all windows sizes, which provided respective AUCs of 91.12%, 91.86% and 90.55%. [Formula: see text] and [Formula: see text] resulted in lower AUCs (below 90%) over all window sizes. [Formula: see text] 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 [Formula: see text] 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 [Formula: see text]-based method. SIGNIFICANCE: Our results demonstrate that the new developed normalized fuzzy entropy is an accurate measure for detecting AF.