Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram

by   Dongdong Zhang, et al.

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


page 3

page 4

page 5

page 6

page 7


Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network

A large number of people suffer from life-threatening cardiac abnormalit...

A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance

The performances of commonly used electrocardiogram (ECG) diagnosis mode...

Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports

Electrocardiogram (ECG) synthesis is the area of research focused on gen...

Generalization Studies of Neural Network Models for Cardiac Disease Detection Using Limited Channel ECG

Acceleration of machine learning research in healthcare is challenged by...

Abductive reasoning as the basis to reproduce expert criteria in ECG Atrial Fibrillation identification

Objective: This work aims at providing a new method for the automatic de...

MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG

Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) sig...

Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention

Heart disease is one of the most common diseases causing morbidity and m...

Please sign up or login with your details

Forgot password? Click here to reset