由于卷积滤波尺寸等限制，U-net无法学习到心电(Electrocardiographic，ECG)信号的长时序关联性以及标签间的相关性。对此提出一种基于U-net-BiLSTM-CRF的心律失常多目标检测方法，可同时输出目标心拍所属类型和位置信息。使用U-net学习融合特征，再将其输入到双向长短时记忆网络(Bi-directional Long Short-Term Memory，BiLSTM)中学习长时序依赖特征，最后使用条件随机场(Conditional Random Field，CRF)对标签间的关系建模，优化分类结果。依据ANSI/AAMI EC57:2012的心搏分类标准，对MIT-BIH心律失常数据库中共85 609个心拍记录进行划分，在划分后数据集上的实验结果表明，该方法对心拍分类的准确率达99。11％，特异性为99。76％，灵敏度为97。21％，优于传统U-net在MIT-BIH心律失常数据库上的分类性能。
MULTI-TARGET DETECTION METHOD FOR ARRHYTHMIA BASED ON U-NET-BILSTM-CRF
Due to limitations such as the size of the convolution filter,U-net cannot learn the long timing correlation of electrocardiographic(ECG)signals and the correlation between tags.Therefore,this paper proposes a multi-target detection method for arrhythmia based on U-net-BiLSTM-CRF,which can simultaneously output the type and location of the target heartbeat.U-net was used to learn the fusion features.The fusion features were input into the bi-directional long short-term memory(BiLSTM)to learn long time-dependent features.Conditional random field(CRF)was used to model the relationship between tags to optimize the classification results.According to the heartbeat classification standard of ANSI/AAMI EC57:2012,a dataset was built in this paper based on a total of 85 609 heartbeat records in the MIT-BIH arrhythmia database,which was used to verify the proposed method.The results show that the accuracy of this method for heartbeat classification is 99.11％,the specificity is 99.76％,and the sensitivity is 97.21％,all of which are better than the classification performance of traditional U-net on the MIT-BIH arrhythmia database.
国家科技期刊平台
NSTL
万方数据
维普
