Detection and classification of arrhythmia using an explainable deep learning model
Autor: | Joon-myoung Kwon, Yoon Ji Lee, Ki-Hyun Jeon, Kyung-Hee Kim, Byung Hee Oh, Yong-Yeon Jo, Jang Hyeon Ban, Soo-Youn Lee, Jinsik Park, Yong Hyeon Cho, Jae-Hyun Shin, Min Seung Jung |
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Rok vydání: | 2021 |
Předmět: |
Computer science
Early detection 030204 cardiovascular system & hematology Machine learning computer.software_genre 03 medical and health sciences Electrocardiography 0302 clinical medicine Deep Learning Feature (machine learning) Humans cardiovascular diseases 030212 general & internal medicine Retrospective Studies Receiver operating characteristic business.industry Deep learning External validation Arrhythmias Cardiac Clinical Practice Tree (data structure) Artificial intelligence Cardiology and Cardiovascular Medicine business computer Algorithms |
Zdroj: | Journal of electrocardiology. 67 |
ISSN: | 1532-8430 |
Popis: | Background Early detection and intervention is the cornerstone for appropriate treatment of arrhythmia and prevention of complications and mortality. Although diverse deep learning models have been developed to detect arrhythmia, they have been criticized due to their unexplainable nature. In this study, we developed an explainable deep learning model (XDM) to classify arrhythmia, and validated its performance using diverse external validation data. Methods In this retrospective study, the Sejong dataset comprising 86,802 electrocardiograms (ECGs) was used to develop and internally variate the XDM. The XDM based on a neural network-backed ensemble tree was developed with six feature modules that are able to explain the reasons for its decisions. The model was externally validated using data from 36,961 ECGs from four non-restricted datasets. Results During internal and external validation of the XDM, the average area under the receiver operating characteristic curves (AUCs) using a 12‑lead ECG for arrhythmia classification were 0.976 and 0.966, respectively. The XDM outperformed a previous simple multi-classification deep learning model that used the same method. During internal and external validation, the AUCs of explainability were 0.925–0.991. Conclusion Our XDM successfully classified arrhythmia using diverse formats of ECGs and could effectively describe the reason for the decisions. Therefore, an explainable deep learning methodology could improve accuracy compared to conventional deep learning methods, and that the transparency of XDM can be enhanced for its application in clinical practice. |
Databáze: | OpenAIRE |
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