摘要
为更加准确地从动态心电中提取异常心拍,设计一种融合卷积神经网络(CNN)和多层双边长短时记忆网络(BiLSTM)的心律失常心拍分类模型。心电信号首先被分割成0.75s和4s两种不同尺度大小的心拍信号,然后利用11层CNN网络和3层BiLSTM网络分别对小/大尺度心拍信号进行特征提取与合并,并使用3层全连接网络对合并特征进行降维,最后利用softmax函数实现分类。针对MIT心律失常数据库异常心拍类型分布不均衡的问题,采用添加随机运动噪声和基线漂移噪声的样本扩展方法,降低模型的过拟合。采用基于患者的5折交叉检验进行模型验证。MIT心律失常数据库116000个心拍的分类结果表明:所建立的模型针对4类心拍(正常、房性早搏、室性早搏、未分类)的识别准确率为90.42%,比单独使用CNN(76.45%)和BiLSTM(83.28%)的模型分别提高13.97%和7.14%。所提出的融合CNN和BiLSTM的心律失常心拍分类模型,相比单一基于CNN模型或者BiLSTM模型的机器学习算法,有更好的异常心拍分类准确率。
In order to extract the abnormal beats more accurately from the dynamic electrocardiograph(ECG),a deep learning model combining convolutional neural network(CNN) and bi-directional long short-term memory network(BiLSTM) was proposed in this study.Firstly,ECG signals were segmented into two types of time window lengths: a small-scale length of 0.75 s and a large-scale length of 4 s.Then,features were extracted from the small-and large-scale length ECG segments using an 11-layer CNN network and a 3-layer BiLSTM network,respectively.Finally,the extracted features were combined and were then reduced using a 3-layer fully connected network.In addition,two data enhancement methods by adding random motion noise and baseline drift were used to attenuate the influence of over-fitting due to the unbalanced data distribution.The proposed model was tested on the MIT arrhythmia database using a patient-based 5-fold cross-validation method,and its accuracy for classifying the 4 types(normal,atrial premature,ventricular premature and unclassified)on 116,000 heartbeats was 90.42%,which was 13.97% and 7.14% higher than the CNN model(76.45%)and BiLSTM model(83.28%),respectively.This study validated that the proposed model with combining CNN and BiLSTM reports higher accuracy than only using CNN or BiLSTM model when performing the abnormal beat classification task.
作者
杨浩
黄茂林
蔡志鹏
姚映佳
李建清
刘澄玉
Yang Hao;Huang Maolin;Cai Zhipeng;Yao Yingjia;Li Jianqing;Liu Chengyu(Lenovo Research,Shenzhen 518057,Guangdong,China;School of Instrument Science and Engineering,Southeast University,Nanjing 210096,China)
出处
《中国生物医学工程学报》
CAS
CSCD
北大核心
2020年第6期719-726,共8页
Chinese Journal of Biomedical Engineering
基金
国家自然科学基金(81871444)
江苏省杰出青年基金(BK20190014)
江苏省重点研发计划项目(BE2017735)。
关键词
心律失常
心拍分类
心电
卷积神经网络
双边长短时记忆网络
arrhythmia
beat classification
electrocardiograms(ECG)
convolutional neural network(CNN)
bi-directional long short-term memory network(BiLSTM)
