Key result
A normalized fuzzy entropy-based detector accurately identified AF with an AUC of 96.76% for 60-beat windows, outperforming other entropy measures.
Why the study?
Does a normalized fuzzy entropy-based detector improve the accuracy of atrial fibrillation detection from RR interval time series compared to other entropy measures?
Does a normalized fuzzy entropy-based detector improve the accuracy of atrial fibrillation detection from RR interval time series compared to other entropy measures?
Absolute Event Rate: 96.76% vs 90.55%
A novel normalized fuzzy entropy algorithm provides highly accurate detection of atrial fibrillation from RR interval time series, outperforming existing entropy-based methods.
May enhance AF detection via ventricular response; leaves open prospective clinical validation before adoption.
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.
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Liu et al. (2018) studied Atrial fibrillation. Normalized fuzzy entropy vs. Sample entropy, fuzzy measure entropy, and coefficient of sample entropy was evaluated on Area under receiver operating characteristic curve (AUC) for classifying AF and non-AF rhythms (60-beat window). A normalized fuzzy entropy-based detector accurately identified AF with an AUC of 96.76% for 60-beat windows, outperforming other entropy measures.
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