Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram

Summary

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.

Graphical abstract

Highlights

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  We develop a deep learning model for the automatic diagnosis of ECG

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  We present benchmark results of 12-lead ECG classification

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  We find out the top performance single lead in diagnosing ECGs

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  We employ the SHAP method to enhance clinical interpretability

Abstract

Medicine; Clinical Finding; Artificial Intelligence