Kuan-Cheng Chang1, Po-Hsin Hsieh2, Mei-Yao Wu3, Yu-Chen Wang4, Jan-Yow Chen5, Fuu-Jen Tsai6, Edward S C Shih7, Ming-Jing Hwang7, Tzung-Chi Huang8. 1. Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan; Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan. Electronic address: kuancheng.chang@gmail.com. 2. Department of Biomedical Imaging and Radiologic Science, China Medical University, Taichung, Taiwan. 3. School of Postbaccalaureate Chinese Medicine, China Medical University, Taichung, Taiwan; Department of Chinese Medicine, China Medical University Hospital, Taichung, Taiwan. 4. Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan; Division of Cardiovascular Medicine, Asia University Hospital, Taichung, Taiwan; Department of Biotechnology, Asia University, Taichung, Taiwan. 5. Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan; Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan. 6. Department of Medical Research, China Medical University Hospital, Taichung, Taiwan. 7. Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan. 8. Department of Biomedical Imaging and Radiologic Science, China Medical University, Taichung, Taiwan; Artificial Intelligence Center, China Medical University Hospital, Taichung, Taiwan; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
Abstract
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 F1 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.
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 F1 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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