基于深度学习并行网络模型的心律失常分类方法

目的 提出一种并行神经网络分类方法，以提高对正常搏动、室上性异位搏动、心室异位搏动、融合搏动4种心律失常的分类性能。方法 首先进行心电信号去噪、小尺度心拍和大尺度心拍的分割、数据增强等预处理；然后基于深度学习理论，应用密集连接卷积神经网络改善人工提取波形特征的局限性，并结合双向长短时记忆网络和高效通道注意力网络，以增强提取波形时序特征和重要特征的功能；接着采用并行网络结构，同时输入小尺度心拍和大尺度心拍的的波形特征，以提高心律失常分类的准确性；最后使用Softmax函数实现对心律失常的4分类任务。结果 利用MIT-BIH心律失常数据库和3组实验验证所提方法。多种并行网络模型分类性能对比实验和不同心拍输入方式下，各分类模型性能对比实验得出所提分类模型的总体准确率、平均灵敏度和平均特异性分别达到99.36%、96.08%、99.41%；并行网络分类模型收敛性能分析实验得出分类模型每次训练时间为41 s。结论 并行多网络分类方法在保持较高总体准确率的同时，平均灵敏度、平均特异性以及训练时间均有改善，该方法有望为心律失常临床诊断提供新的技术方案。

An arrhythmia classification method based on deep learning parallel network model

Objective We propose a parallel neural network classification method to improve the performance of classification of 4 types of arrhythmias: normal beat, supraventricular ectopic beat, ventricular ectopic beat and fused beat. Methods Preprocessing was performed including denoising of ECG signal, segmentation of small-scale heartbeat and large-scale heartbeat and data enhancement. Based on deep learning theory, densely connected convolutional network was applied to improve the limitation of waveform feature extraction, and bidirectional long short-term memory network and efficient channel attention network were combined to enhance the function of time series features and important features of the waveform. The parallel network structure was adopted, and the waveform features of small- scale heartbeat and large-scale heartbeat were input to improve the accuracy of arrhythmia classification at the same time. Softmax was used to carry out the 4 classification tasks of arrhythmia by the parallel network model. Results The proposed method was verified using MIT-BIH Arrhythmia Database and 3 groups of experiments. The experiments for comparing the classification performance of multiple parallel network models and that of each classification model under different heartbeat input methods showed that the proposed classification model had an overall accuracy, average sensitivity and average specificity of 99.36% , 96.08% and 99.41%, respectively. Convergence performance analysis of the parallel network classification model showed that the training time of the classification model was 41 s. Conclusion The parallel multi-network classification method can improve the average sensitivity, specificity and training time while maintaining a high overall accuracy, and may thus provide a new technical solution for clinical diagnosis of arrhythmia.