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A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals 期刊论文

编号：

02f1a0d8-a059-498c-ac4d-69c0fe901467
作者：

Dang, Hao#^[1,2]Sun, Muyi^[1,2];Zhang, Guanhong^[1,2];Qi, Xingqun^[1,2];Zhou, Xiaoguang*^[1,2]Chang, Qing(常庆)*^[3,4]
语种：

英文
期刊：

IEEE ACCESS ISSN：2169-3536 2019 年 7 卷 (75577 - 75590)
收录：
关键词：

Convolution neural network deep learning bi-directional long short-term memory atrial fibrillation
摘要：

Atrial fibrillation (AF), a common abnormal heartbeat rhythm, is a life-threatening recurrent disease that affects older adults. Automatic classification is one of the most valuable topics in medical sciences and bioinformatics, especially the detection of atrial fibrillation. However, it is difficult to accurately explain the local characteristics of electrocardiogram (ECG) signals by manual analysis, due to their small amplitude and short duration, coupled with the complexity and non-linearity. Hence, in this paper, we propose a novel deep arrhythmia-diagnosis method, named deep CNN-BLSTM network model, to automatically detect the AF heartbeats using the ECG signals. The model mainly consists of four convolution layers: two BLSTM layers and two fully connected layers. The datasets of RR intervals (called set A) and heartbeat sequences (P-QRS-T waves, called set B) are fed into the above-mentioned model. Most importantly, our proposed approach achieved favorable performances with an accuracy of 99.94% and 98.63% in the training and validation set of set A, respectively. In the testing set (unseen data sets), we obtained an accuracy of 96.59%, a sensitivity of 99.93%, and a specificity of 97.03%. To the best of our knowledge, the algorithm we proposed has shown excellent results compared to many state-of-art researches, which provides a new solution for the AF automatic detection.
推荐引用方式
GB/T 7714：

Dang Hao,Sun Muyi,Zhang Guanhong, et al. A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals [J].IEEE ACCESS,2019,7:75577-75590.
APA：

Dang Hao,Sun Muyi,Zhang Guanhong,Qi Xingqun,&Chang Qing.(2019).A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals .IEEE ACCESS,7:75577-75590.
MLA：

Dang Hao, et al. "A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals" .IEEE ACCESS 7(2019):75577-75590.
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