摘要
目的探讨BiLSTM-Attention混合神经网络模型在心律失常预测中的应用价值。方法选取中国心血管疾病数据库27036条心电图(Electrocardiogram,ECG)数据,按照8∶1∶1的比例划分为训练集、验证集和测试集,采用中值滤波法与小波变换阈值法对原始ECG数据进行降噪预处理,采用BiLSTM对数据进行特征学习,融合注意力机制分配注意力权重,构建BiLSTM-Attention模型进行心律失常分类预测。将BiLSTM-Attention模型与长短期记忆网络(Long Short-Term Memory,LSTM)、LSTM-Attention和BiLSTM模型进行对比,采用F1分数和曲线下面积(Area Under Curve,AUC)对模型进行评价。结果BiLSTM-Attention模型总体的F1分数为0.799,心房颤动、一度房室传导阻滞、窦性心律失常、窦性心律均获得了较高的F1分数,分别为0.955、0.862、0.954和0.917,9类心律失常的AUC均大于0.87。结论BiLSTM-Attention心律失常分类模型具备较强的分类能力,对部分心律失常有较强的识别能力,经训练后能更好地辅助临床进行心律失常诊断,具备一定的实用价值。
Objective To explore the application value of the BiLSTM-Attention hybrid neural network model in the prediction of arrhythmia.Methods A total of 27036 electrocardiogram(ECG)data from the Chinese Cardiovascular Disease Database were selected and divided into training set,validation set and test set according to the ratio of 8∶1∶1.The median filter method and wavelet transform threshold method were used to denoise and preprocess the original ECG data.The BiLSTM model was used to learn the features of the data,and the attention mechanism was integrated to allocate the weight.The BiLSTM-Attention model was constructed to predict the classification of arrhythmia.The BiLSTM-Attention model was compared with long short-term memory(LSTM),LSTM-Attention and BiLSTM models,and F1 score and area under curve(AUC)were used to evaluate the model.Results The F1 score of the BiLSTM-Attention model was 0.799.The atrial fibrillation,first degree atrioventricular block,sinus rhythm abnormalities,and sinus rhythm obtained high F1 scores,which were 0.955,0.862,0.954,and 0.917 respectively.The AUC of nine types of arrhythmia was greater than 0.87.Conclusion The BiLSTM-Attention arrhythmia classification model has strong classification ability and strong recognition ability for some arrhythmia abnormalities.After training,it can better assist clinical diagnosis of arrhythmia,and has certain practical value.
作者
杜丛强
崔昊
DU Congqiang;CUI Hao(Information Center,Affiliated Hospital of Shandong University of Traditional Chinese Medicine,Jinan Shandong 250011,China;Bidding Office,Affiliated Hospital of Shandong University of Traditional Chinese Medicine,Jinan Shandong 250011,China)
出处
《中国医疗设备》
2023年第11期67-72,共6页
China Medical Devices
