A hierarchical transformer for electrocardiogram classification and diagnosis
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1311Keywords:
Electrocardiogram, Automatic Diagnosis, Neural networksAbstract
Objective: Electrocardiogram (ECG) is an important tool to assess cardiac conditions. The advancement of artificial intelligence has enabled progress in the automatic analysis of ECGs. Aiming to improve the predictive performance of automatic diagnosis, this paper presents a new hierarchical transformer model (HiT) for classifying 12-lead ECGs. Method: The HiT model integrates convolutional and transformer blocks - specifically designed with local attention mechanisms - to guide the learning of local and global features of ECG signals. Results: Using a subset of CODE, a broad ECG database from Brazil, the model was developed for classifying six cardiac conditions and achieved an average f1-score over 0.84, surpassing the state of the art for the same data. Conclusion: Therefore, this work demonstrates the potential of a multi-level hierarchical transformer for more accurate automatic diagnosis of heart diseases.
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