Myocardial Infarction Detection and Localization with Electrocardiogram Based on Convolutional Neural Network

作     者：LIU Jikui WANG Ruxin WEN Bo LIU Zengding MIAO Fen LI Ye LIU Jikui;WANG Ruxin;WEN Bo;LIU Zengding;MIAO Fen;LI Ye

作者机构：Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen College of Advanced Technology University of Chinese Academy of Sciences 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2021年第30卷第5期

页      面：833-842页

核心收录：

学科分类：12[管理学] 08[工学] 1010[医学-医学技术（可授医学、理学学位）] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 081104[工学-模式识别与智能系统] 080203[工学-机械设计及理论] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：supported by the the National Natural Science Foundation of China (No.61771465, No.U1801261, No.81701788) Strategic Priority CAS Project (No.XDB38040200) Shenzhen Science and Technology Program (No.JCYJ20180703145002040) 

主　　题：medical signal processing waveform change features convolutional neural network feature extraction multiscale spatiotemporal feature extraction method electrocardiogram MI automatic diagnosis ECG signal myocardial infarction detection MI-CNN model electrocardiography myocardial infarction diagnosis learning (artificial intelligence) medical signal detection MI detection MI localization ECG image multiresolution time series wavelet transforms wavelet decomposition LL-CNN model 

摘      要：Electrocardiogram(ECG) is widely used in Myocardial infarction(MI) diagnosis. The automatic diagnosis of MI based on the 12-lead ECG needs to consider not only the waveform change features in multi-resolution time series, but also the spatial correlation information between the leads. To this end, this work proposed multiscale spatiotemporal feature extraction method based on Convolutional neural network(CNN) for MI automatic diagnosis. First, the 12-lead ECG is first transformed into an ECG image through wavelet decomposition and 3-dimensional space reconstruction. The MI-CNN model is then constructed to identify MI using 41368 ECG ***, we develop the LL-CNN model, which is utilized only after the ECG signal is identified as an MI event by the MI-CNN model, to localize MI by employing transfer learning to overcome the limited data problem. The proposed method has achieved an accuracy of 99.51% on MI detection, and a macro-F1 of 99.14% on MI ***, the features visualization shows that U-wave has significant diagnostic value for MI. The proposed method significantly improves the performance of MI detection and localization compared with other methods. It is promising to be used for MI monitoring and diagnosis.