基于一维卷积神经网络的房颤智能诊断方法研究

针对“大数据”时代如何利用数据对房颤进行智能、高效的诊断问题,提出了基于一维卷积神经网络的智能诊断方法,以避免传统算法依赖人工特征提取和先验知识的问题。首先,分别构建一维LeNet-5和AlexNet神经网络模型,合理设置网络结构参数;然后,在采集的实验数据基础上针对心电信号的特点进行一系列的数据处理,随机构建训练样本和测试样本;最后,将训练样本分别输入上述2个神经网络模型中训练学习,再将训练好的模型用于房颤的诊断。实验结果表明:一维LeNet-5网络模型存在“过拟合”现象,而一维AlexNet网络模型在避免了上述现象的同时,诊断精度达到了95.34%,较传统方法有了较大提升,为房颤诊断提供了有效的手段。

Research on Intelligent Diagnosis of Atrial Fibrillation Based on One-dimensional Convolution Neural Network

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.