基于CNN与BiLSTM相结合的心电信号分类方法

ECG signal classification method based on the combination of CNN and BiLSTM network

目的:基于卷积神经网络(CNN)与双向长短期记忆网络(BiLSTM)相结合的方法在心电信号数据集上训练深度学习模型,实现对心电信号的高效分类。方法:提出CNN+BiLSTM模型实现心电信号数据集分类,采用CNN对心电信号进行初步特征提取;通过BiLSTM对这些特征进行更深层次地学习,以捕捉信号中的时序信息和空间关联性。结果:通过对比实验证明CNN与BiLSTM对模型性能提升的有效性,模型分类精度为98.91%,召回率为98.71%,精确度为99.79%,F1指数为99.14%。结论:将CNN与BiLSTM相结合并应用在心电信号数据集分类,能有效提升临床诊断的精确性,为心电信号的自动分析提供了有效参考。

Objective To train a deep learning model based on the combination of convolutional neural network(CNN)and bi-directional long and short-term memory(BiLSTM)network on electrocardiogram(ECG)signal dataset to achieve efficient classification of ECG signals.Methods The CNN+BiLSTM model was proposed to classify ECG signal dataset,and CNN was used to extract the initial features of ECG signals.These features were learned more deeply through a BiLSTM network to capture the temporal information and spatial correlation in the signal.Results Through comparative experiment,the CNN and BiLSTM network were effective in improving the model performance.The classification accuracy of the model was 98.91%,the recall rate was 98.71%,the accuracy was 99.79%,and the F1 index was 99.14%.Conclusion Integrating and applying CNN and BiLSTM network to classify ECG signal dataset can effectively improve the flexibility of clinical diagnosis,and provide an effective reference for the automatic analysis of ECG signals.