Abstract

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Title:: Atrial fibrillation detection based on long-term RR interval sequences of electrocardiogram

Author(s):: FANG Dongshen1; YE Qiyao1; SHI Shaobo2; LIU Tao1; LI Li1; 1. School of Electronic Information, Wuhan University, Wuhan 430072, China 2. Renmin Hospital, Wuhan University, Wuhan 430060, China

Keywords:: Keywords: deep learning atrial fibrillation electrocardiogram signal RR interval convolutional neural network long short-term memory network

Abstract:: Abstract: An atrial fibrillation detection algorithm based on long-time RR interval is proposed to solve the problem of the poor generalization ability of current deep learning models for atrial fibrillation detection. Based on the one-dimensional timing characteristics of electrocardiogram signals and the specific RR interval of atrial fibrillation, a deep learning model combining convolutional neural network and long short-term memory network is designed to deeply mine the temporal and spatial characteristics of long-term RR sequences, which enables the model to perform well on unknown data sets. All available samples of the MIT-BIH atrial fibrillation data set are divided into training, validation, and blindfold detection test sets (3 subjects). After 10-fold cross-validation, the accuracy, sensitivity, specificity, positive prediction rate, and F1 score on the blind detection test set are 99.11%, 98.86%, 99.47%, 99.62%, and 99.24%, respectively. The comparison with the existing methods confirms the feasibility of the proposed model for atrial fibrillation detection. The proposed method can effectively identify atrial fibrillation cases from unknown data sets and has a high generalization ability.