Why the study?
Current automatic analysis in multi-lead ECG equipment is not accurate enough for discriminating abnormal ECG events, requiring further checks by doctors.
The Multi-ECGNet deep learning model demonstrates high accuracy (micro-F1-score 0.863) in the automated multi-label classification of 55 different arrhythmias from 12-lead ECGs.
AI-enhanced multi-lead ECG analysis may aid automated screening; leaves open need for rigorous prospective validation before practice change.
With the development of various deep learning algorithms, the importance and potential of AI + medical treatment are increasingly prominent. Electrocardiogram (ECG) as a common auxiliary diagnostic index of heart diseases, has been widely applied in the pre-screening and physical examination of heart diseases due to its low price and non-invasive characteristics. Currently, the multi-lead ECG equipments have been used in the clinic, and some of them have the automatic analysis and diagnosis functions. However, the automatic analysis is not accurate enough for the discrimination of abnormal events of ECG, which needs to be further checked by doctors. We therefore develop a deep-learning-based approach for multi-label classification of ECG named Multi-ECGNet, which can effectively identify patients with multiple heart diseases at the same time. The experimental results show that the performance of our methods can get a high score of 0.863 (micro-F1-score) in classifying 55 kinds of arrythmias, which is beyond the level of ordinary human experts.
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Cai et al. (2020) studied this question.
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