IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG
Classification
- URL: http://arxiv.org/abs/2204.05116v1
- Date: Wed, 6 Apr 2022 16:29:10 GMT
- Title: IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG
Classification
- Authors: Likith Reddy, Vivek Talwar, Shanmukh Alle, Raju. S. Bapi, U. Deva
Priyakumar
- Abstract summary: In clinical settings, a cardiologist makes a diagnosis based on the standard 12-channel ECG recording.
We propose a model that leverages the multiple-channel information available in the standard 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.
- Score: 0.9449650062296824
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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.
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