Aprimoramento do diagnóstico automatizado de eletrocardiograma (ECG) por meio de pré-treinamento multimodal com laudos em texto

Objective: Heart diseases are the leading cause of death worldwide, and the electrocardiogram (ECG) is the primary diagnostic tool for assessing cardiac activity. Automated and remote ECG diagnosis can help the healthcare system with timely and high-quality cardiac assessments, especially for peripheral regions and rural areas. Automatic ECG classification has been extensively researched, but it is still challenging to build accurate models for such a wide spectrum of scenarios. Method: This study enhances the performance of ECG deep learning classification models using a multimodal pre-training stage with physician's reports. Results: Our approach improves the state-of-the-art model and achieves a mean F1 score of 0.755 over six categories using the full dataset, which is a relevant improvement for a relatively larger unlabeled corpus. Conclusion: The results demonstrate the potential to improve automated cardiac assessment with text pretraining.