Research on Intelligent Diagnosis of Atrial Fibrillation Based on One-dimensional Convolution Neural Network
谢胜龙1,2,3,4,张为民2,鲁玉军4,张文欣2,朱俊江1,任国营3
1. School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
2. Zhejiang Xizi Heavy Machinery Co. Ltd., Jiaxing, Zhejiang 314423, China
3. National Institute of Metrology, Beijing 100029, China
4. Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
Abstract：Aiming at the problem of how to use data to diagnose atrial fibrillation intelligently and efficiently in the era of “big data”, an intelligent fault diagnosis method based on one-dimensional LeNet-5 convolution neural network(CNN) is proposed to avoid the problem that traditional algorithms rely on artificial feature extraction and prior knowledge. Firstly, one-dimensional LeNet-5 and AlexNet neural network models are constructed, and network structure parameters are set reasonably. Then, a series of data processing is carried out according to the characteristics of ECG signals based on the collected experimental data, and training samples and test samples are constructed randomly. Finally, the training samples are input into the above two convolution neural network models for training and learning, and the trained model is applied to the diagnosis of atrial fibrillation. The experimental results show that the one-dimensional LeNet-5 network model has the phenomenon of “over-fitting”, while the one-dimensional AlexNet network model avoids the above phenomena and achieves a diagnostic accuracy of 95.34%. Compared with the traditional methods, the intelligent diagnosis method has been improved accuracy greatly, and it provides effective means for the identification of atrial fibrillation.
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2020, Vol. 41 
