Abstract：The onset of atrial fibrillation is sudden and often accompanied with serious complications (such as stroke and myocardial infarction) which greatly threats health. With the development of deep learning technology, deep neural network has been widely used in atrial fibrillation classification algorithm due to its advantages including automatic feature extraction. This paper proposes a deep learning framework based on convolutional neural network (CNN) and bi-directional long shortterm memory (Bi-LSTM) network. It could predict atrial fibrillation according to ECG. By using CNN, we extracted the morphological characteristics of ECG signals and made sequence reconstruction. The reconstructed sequence was input into Bi-LSTM network, and the rhythm changes of positive and negative timing were analyzed. It could effectively predict the data within 30 minutes before the occurrence of atrial fibrillation, normal sinus rhythm data and the data at the onset of atrial fibrillation. The proposed algorithm was trained and validated with the data of longterm atrial fibrillation database, MIT-BIH atrial fibrillation database and MIT-BIH normal sinus rhythm database; the accuracy reached 93.3%.
王量弘，李馨，陈钧颖，杨涛，王新康，高洁. 结合卷积神经网络与双向长短期记忆网络的房颤预测算法研究[J]. 实用心电学杂志, 2022, 31(4): 256-261.
WANG Lianghong, LI Xin, CHEN Junying, YANG Tao, WANG Xinkang, GAO Jie. Study on atrial fibrillation prediction algorithm combining convolutional neural network and bi-directional long short-term memory network. JOURNAL OF PRACTICAL ELECTROCARDIOLOGY, 2022, 31(4): 256-261.
