A deep neural network for automatic classification of cardiac arrhythmias from 12-lead ECGs achieved an average F1 score of 0.813, outperforming 4 traditional machine learning methods.
Does a deep neural network improve automatic classification of cardiac arrhythmias from 12-lead ECGs compared to traditional machine learning methods?
A deep neural network using all 12 leads simultaneously achieved an average F1 score of 0.813 for automatic arrhythmia classification, outperforming traditional machine learning methods.
Electrocardiogram (ECG) is a widely used reliable, non-invasive approach for cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the insufficiency of cardiologists, accurate and automatic diagnosis of ECG signals has become a hot research topic. In this paper, we developed a deep neural network for automatic classification of cardiac arrhythmias from 12-lead ECG recordings. Experiments on a public 12-lead ECG dataset showed the effectiveness of our method. The proposed model achieved an average F1 score of 0.813. The deep model showed superior performance than 4 machine learning methods learned from extracted expert features. Besides, the deep models trained on single-lead ECGs produce lower performance than using all 12 leads simultaneously. The best-performing leads are lead I, aVR, and V5 among 12 leads. Finally, we employed the SHapley Additive exPlanations method to interpret the model's behavior at both the patient level and population level.
Zhang et al. (2021) studied Cardiac arrhythmias. Deep neural network vs. 4 machine learning methods using expert features was evaluated on Average F1 score. A deep neural network for automatic classification of cardiac arrhythmias from 12-lead ECGs achieved an average F1 score of 0.813, outperforming 4 traditional machine learning methods.
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