IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification
This work addresses early detection of cardiovascular diseases for clinical diagnosis, but it is incremental as it builds on existing deep learning methods by incorporating multi-channel analysis.
The paper tackled ECG classification by leveraging multi-channel information from standard 12-channel recordings, achieving a macro-averaged ROC-AUC score of 0.9216 and mean accuracy of 88.85% on the PTB-XL dataset.
Early detection of cardiovascular diseases is crucial for effective treatment and an electrocardiogram (ECG) is pivotal for diagnosis. The accuracy of Deep Learning based methods for ECG signal classification has progressed in recent years to reach cardiologist-level performance. In clinical settings, a cardiologist makes a diagnosis based on the standard 12-channel ECG recording. Automatic analysis of ECG recordings from a multiple-channel perspective has not been given enough attention, so it is essential to analyze an ECG recording from a multiple-channel perspective. We propose a model that leverages the multiple-channel information available in the standard 12-channel ECG recordings and learns patterns at the beat, rhythm, and channel level. The experimental results show that our model achieved a macro-averaged ROC-AUC score of 0.9216, mean accuracy of 88.85\%, and a maximum F1 score of 0.8057 on the PTB-XL dataset. The attention visualization results from the interpretable model are compared against the cardiologist's guidelines to validate the correctness and usability.