Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram
This work addresses the need for accurate and automated ECG diagnosis to assist cardiologists, but it is incremental as it applies existing deep learning and interpretability techniques to a specific medical dataset.
The paper tackled the problem of automatic diagnosis of cardiac arrhythmias from 12-lead ECG signals using a deep neural network, achieving an average AUC of 0.970 and an average F1 score of 0.813, outperforming machine learning methods based on expert features.
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. Deep learning methods have demonstrated promising results in predictive healthcare tasks. In this paper, we developed a deep neural network for multi-label classification of cardiac arrhythmias in 12-lead ECG recordings. Experiments on a public 12-lead ECG dataset showed the effectiveness of our method. The proposed model achieved an average area under the receiver operating characteristic curve (AUC) of 0.970 and 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 (SHAP) method to interpret the model's behavior at both patient level and population level. Our code is freely available at https://github.com/onlyzdd/ecg-diagnosis.