A novel lightweight computerized ECG interpretation approach based on clinical 12-lead data

被引:0
作者
YunQing Liu
ChengJin Qin
JinLei Liu
YanRui Jin
ZhiYuan Li
LiQun Zhao
ChengLiang Liu
机构
[1] Shanghai Jiao Tong University,School of Mechanical Engineering
[2] Shanghai Jiao Tong University,Key Lab of Artificial Intelligence (Ministry of Education), AI Institute
[3] Shanghai First People’s Hospital Affiliated to Shanghai Jiao Tong University,Department of Cardiology
来源
Science China Technological Sciences | 2024年 / 67卷
关键词
computerized ECG interpretation; large-scale 12-lead clinical ECG database; lightweight neural network;
D O I
暂无
中图分类号
学科分类号
摘要
Although 12-lead electrocardiograms (ECGs) provide a wide range of spatiotemporal characteristics, interpreting them for arrhythmia detection is difficult due to a lack of reliable large-scale clinical datasets. Herein, we proposed an innovative lightweight computerized ECG interpretation approach based on 12-lead data. Our model was trained, validated, and tested on 53845 standard 12-lead ECG records collected at Shanghai First People’s Hospital in affiliation with Shanghai Jiao Tong University. The experiments revealed that our approach had a classification accuracy of 94.41% in the classification task of seven types of rhythms, which was markedly superior to related single-lead and 12-lead ECG classification methods. Moreover, the average receiver operating characteristic area under the curve reached a value of 0.940, and the precision values for sinus tachycardia and sinus bradycardia were 0.945 and 0.91, respectively, with specificity values of 0.996 and 0.994. By employing our boosting method, we were able to improve the accuracy to 94.85%. To investigate the performance degradation of the proposed neural network in some classes, an ECG cardiologist was enlisted to review questionable ECGs; this process provides a promising direction for network performance improvement. Therefore, the proposed computerized ECG interpretation approach has practical significance because it could help professional physicians analyze patients’ heart conditions based on real-time 12-lead ECG or grade their disease severity in advance.
引用
收藏
页码:449 / 463
页数:14
相关论文
共 130 条
[1]  
Jin Y R(2022)Multi-class 12-lead ECG automatic diagnosis based on a novel subdomain adaptive deep network Sci China Tech Sci 65 2617-2630
[2]  
Li Z Y(2022)Multiple high-regional-incidence cardiac disease diagnosis with deep learning and its potential to elevate cardiologist performance iScience 25 105434-1037
[3]  
Liu Y Q(2018)A highly conductive and stretchable wearable liquid metal electronic skin for long-term conformable health monitoring Sci China Tech Sci 61 1031-1129
[4]  
Liu Y(2020)A deep learning system to screen novel coronavirus disease 2019 pneumonia Engineering 6 1122-285
[5]  
Qin C(2019)A synchroextracting-based method for early chatter identification of robotic drilling process Int J Adv Manuf Technol 100 273-182
[6]  
Liu C(2020)Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network Inf Fusion 53 174-2006
[7]  
Guo R(2022)Flexible hybrid electronics: Enabling integration techniques and applications Sci China Tech Sci 65 1995-e200
[8]  
Wang X L(2000)Physiobank, physiotoolkit, and physionet Circulation 101 e215-50
[9]  
Yu W Z(2001)The impact of the mit-bih arrhythmia database IEEE Eng Med Biol Mag 20 45-282
[10]  
Xu X(2013)Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform Knowledge-Based Syst 37 274-2775