# Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram

arXiv: https://arxiv.org/abs/2010.10328 · HTML: https://mlsanity.com/paper/2010.10328/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification](https://mlsanity.com/paper/2207.12381/) (2022)
- [HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN](https://mlsanity.com/paper/2303.06020/) (2023)
- [Arrhythmia Classification from 12-Lead ECG Signals Using Convolutional and Transformer-Based Deep Learning Models](https://mlsanity.com/paper/2502.17887/) (2025)
- [ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification](https://mlsanity.com/paper/2505.03787/) (2025)
- [SIM-ECG: A Signal Importance Mask-driven ECGClassification System](https://mlsanity.com/paper/2110.14835/) (2021)
- [A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance](https://mlsanity.com/paper/2208.00323/) (2022)
- [Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat Counting and Demographic Data Integration](https://mlsanity.com/paper/2208.07088/) (2022)
- [Machine learning-based detection of cardiovascular disease using ECG signals: performance vs. complexity](https://mlsanity.com/paper/2303.11429/) (2023)
- [Cardi-GPT: An Expert ECG-Record Processing Chatbot](https://mlsanity.com/paper/2510.24737/) (2025)
- [Sampling Matters: The Effect of ECG Frequency on Deep Learning-Based Atrial Fibrillation Detection](https://mlsanity.com/paper/2604.16437/) (2026)
- [MSAIC-Net: A Multi-Scale Attention and Imbalance-Aware Contrastive Network for ECG-Based Myocardial Substrate Abnormality Detection](https://mlsanity.com/paper/2606.06718/) (2026)

## Abstract (arXiv)

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.