Accurate detection of atrial fibrillation events with R-R intervals from ECG signals

Author:

Duan JunboORCID,Wang Qing,Zhang Bo,Liu Chen,Li Chenrui,Wang Lei

Abstract

Atrial fibrillation (AF) is a typical category of arrhythmia. Clinical diagnosis of AF is based on the detection of abnormal R-R intervals (RRIs) with an electrocardiogram (ECG). Previous studies considered this detection problem as a classification problem and focused on extracting a number of features. In this study we demonstrate that instead of using any specific numerical characteristic as the input feature, the probability density of RRIs from ECG conserves comprehensive statistical information; hence, is a natural and efficient input feature for AF detection. Incorporated with a support vector machine as the classifier, results on the MIT-BIH database indicates that the proposed method is a simple and accurate approach for AF detection in terms of accuracy, sensitivity, and specificity.

Funder

National Science Foundation of China

Provincial Science Foundation of Shaanxi

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

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