Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms

被引:41
|
作者
Chang, Kuan-Cheng [1 ,2 ]
Hsieh, Po-Hsin [3 ]
Wu, Mei-Yao [4 ,5 ]
Wang, Yu-Chen [1 ,6 ,7 ]
Chen, Jan-Yow [1 ,2 ]
Tsai, Fuu-Jen [8 ]
Shih, Edward S. C. [9 ]
Hwang, Ming-Jing [9 ]
Huang, Tzung-Chi [3 ,10 ,11 ]
机构
[1] China Med Univ Hosp, Div Cardiovasc Med, 2 Yude Rd, Taichung 40447, Taiwan
[2] China Med Univ, Grad Inst Biomed Sci, Taichung, Taiwan
[3] China Med Univ, Dept Biomed Imaging & Radiol Sci, Taichung, Taiwan
[4] China Med Univ, Sch Postbaccalaureate Chinese Med, Taichung, Taiwan
[5] China Med Univ Hosp, Dept Chinese Med, Taichung, Taiwan
[6] Asia Univ Hosp, Div Cardiovasc Med, Taichung, Taiwan
[7] Asia Univ, Dept Biotechnol, Taichung, Taiwan
[8] China Med Univ Hosp, Dept Med Res, Taichung, Taiwan
[9] Acad Sinica, Inst Biomed Sci, Taipei, Taiwan
[10] China Med Univ Hosp, Artificial Intelligence Ctr, Taichung, Taiwan
[11] Asia Univ, Dept Bioinformat & Med Engn, Taichung, Taiwan
关键词
BIDIRECTIONAL LSTM; DEEP; CARDIOLOGY; COMMITTEE; IMAGES;
D O I
10.1016/j.cjca.2020.02.096
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify different types of cardiac arrhythmias with the use of a single-lead ECG input data set have been developed. It remains to be determined whether these algorithms can be generalized to 12-lead ECG-based rhythm classification. Methods: We used a long short-term memory (LSTM) model to detect 12 heart rhythm classes with the use of 65,932 digital 12-lead ECG signals from 38,899 patients, using annotations obtained by consensus of 3 board-certified electrophysiologists as the criterion standard. Results: The accuracy of the LSTM model for the classification of each of the 12 heart rhythms was >= 0.982 (range 0.982-1.0), with an area under the receiver operating characteristic curve of >= 0.987 (range 0.987-1.0). The precision and recall ranged from 0.692 to 1 and from 0.625 to 1, respectively, with an F-1 score of >= 0.777 (range 0.777-1.0). The accuracy of the model (0.90) was superior to the mean accuracies of internists (0.55), emergency physicians (0.73), and cardiologists (0.83). Conclusions: We demonstrated the feasibility and effectiveness of the deep-learning LSTM model for interpreting 12 common heart rhythms according to 12-lead ECG signals. The findings may have clinical relevance for the early diagnosis of cardiac rhythm disorders.
引用
收藏
页码:94 / 104
页数:11
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