共 24 条
Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms
被引:46
作者:

Chang, Kuan-Cheng
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China Med Univ Hosp, Div Cardiovasc Med, 2 Yude Rd, Taichung 40447, Taiwan
China Med Univ, Grad Inst Biomed Sci, Taichung, Taiwan China Med Univ Hosp, Div Cardiovasc Med, 2 Yude Rd, Taichung 40447, Taiwan

Hsieh, Po-Hsin
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机构:
China Med Univ, Dept Biomed Imaging & Radiol Sci, Taichung, Taiwan China Med Univ Hosp, Div Cardiovasc Med, 2 Yude Rd, Taichung 40447, Taiwan

Wu, Mei-Yao
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机构:
China Med Univ, Sch Postbaccalaureate Chinese Med, Taichung, Taiwan
China Med Univ Hosp, Dept Chinese Med, Taichung, Taiwan China Med Univ Hosp, Div Cardiovasc Med, 2 Yude Rd, Taichung 40447, Taiwan

Wang, Yu-Chen
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机构:
China Med Univ Hosp, Div Cardiovasc Med, 2 Yude Rd, Taichung 40447, Taiwan
Asia Univ Hosp, Div Cardiovasc Med, Taichung, Taiwan
Asia Univ, Dept Biotechnol, Taichung, Taiwan China Med Univ Hosp, Div Cardiovasc Med, 2 Yude Rd, Taichung 40447, Taiwan

Chen, Jan-Yow
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China Med Univ Hosp, Div Cardiovasc Med, 2 Yude Rd, Taichung 40447, Taiwan
China Med Univ, Grad Inst Biomed Sci, Taichung, Taiwan China Med Univ Hosp, Div Cardiovasc Med, 2 Yude Rd, Taichung 40447, Taiwan

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Shih, Edward S. C.
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Acad Sinica, Inst Biomed Sci, Taipei, Taiwan China Med Univ Hosp, Div Cardiovasc Med, 2 Yude Rd, Taichung 40447, Taiwan

Hwang, Ming-Jing
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Acad Sinica, Inst Biomed Sci, Taipei, Taiwan China Med Univ Hosp, Div Cardiovasc Med, 2 Yude Rd, Taichung 40447, Taiwan

Huang, Tzung-Chi
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h-index: 0
机构:
China Med Univ, Dept Biomed Imaging & Radiol Sci, Taichung, Taiwan
China Med Univ Hosp, Artificial Intelligence Ctr, Taichung, Taiwan
Asia Univ, Dept Bioinformat & Med Engn, Taichung, Taiwan China Med Univ Hosp, Div Cardiovasc Med, 2 Yude Rd, Taichung 40447, Taiwan
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.
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页码:94 / 104
页数:11
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