中国医疗设备2018，Vol.33Issue(3)：11-14,4.DOI:10.3969/j.issn.1674-1633.2018.03.003
基于一维卷积神经网络的患者特异性心拍分类方法研究
Patient-Specific ECG Classification Based on One-Dimensional Convolution Neural Network
摘要
Abstract
Objective In this paper, we proposed a patient-specific electrocardiograms (ECG) classification method based on one-dimensional convolution neural network to improve the performance about the automatic classification of heart beat, especially the supraventricular ectopic beats. It would provide an auxiliary basis for clinical ECG diagnosis. Methods We combined the ECG features of the multi-layer one-dimensional convolution neural network and the RR interval characteristics of ECG, and then they were sent them into the multi-layer sensor. After that we classified the features processed by softmax classifier. In order to achieve a better patient-specific heart beat recognition, we added the specific-patient ECG data into the original part of the model training data from the public data set. Results Compared with the existing research results, the performance of classification using the MIT-BIH arrhythmia database was improved. The sensitivity of SVEB recognition was increased from 60.3% to 88.7%. Conclusion This method can provide a reliable basis for the diagnosis of heart disease.关键词
心电分类/一维卷积神经网络/特征融合/患者特异性Key words
electrocardiograms classification/one-dimensional convolution neural network/feature fusion/patient-specific分类
医药卫生引用本文复制引用
黄佼,宾光宇,吴水才..基于一维卷积神经网络的患者特异性心拍分类方法研究[J].中国医疗设备,2018,33(3):11-14,4.基金项目
国家自然科学基金项目(71661167001 （）
71781260096) （）
北京工业大学研究生工程实训平台及产学研联合培养基地建设项目(015000514117506) （015000514117506）
北京工业大学研究生科技基金(015000514117502). （015000514117502）
