Publicação
Binary classification of cardiac pathologies using deep learning: a PTB-XL dataset approach
| Resumo: | Cardiovascular diseases, such as myocardial infarction, are among the leading causes of death worldwide. Accuracy and time are crucial for diagnosing these conditions and for effective treatment, usually requiring time-consuming manual analysis of clinical-grade electrocardiogram (ECG). This paper presents a novel deep learning-based method for binary classification of cardiac patholo-gies using the PTB-XL dataset. The model integrates EfficientNetB3 for spatial feature extraction and a Linformer block to capture long-range dependencies be-tween leads. Preprocessing involves converting RGBA ECG images to RGB for-mat and normalizing them to meet the requirements of the inputs of the layers. Initial experiments have shown promising results, achieving an AUC (Area Un-der the Curve) of 86.06%. Further work includes tests to optimize the model's performance based on different key metrics, including accuracy and precision. |
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| Autores principais: | Chaabani, Mohamed |
| Outros Autores: | Guerreiro, Nathan; Ribeiro, Luiz; Luiz, Luiz E.; Slim, Mohamed; Teixeira, João Paulo |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | Cardiovascular diseases, such as myocardial infarction, are among the leading causes of death worldwide. Accuracy and time are crucial for diagnosing these conditions and for effective treatment, usually requiring time-consuming manual analysis of clinical-grade electrocardiogram (ECG). This paper presents a novel deep learning-based method for binary classification of cardiac patholo-gies using the PTB-XL dataset. The model integrates EfficientNetB3 for spatial feature extraction and a Linformer block to capture long-range dependencies be-tween leads. Preprocessing involves converting RGBA ECG images to RGB for-mat and normalizing them to meet the requirements of the inputs of the layers. Initial experiments have shown promising results, achieving an AUC (Area Un-der the Curve) of 86.06%. Further work includes tests to optimize the model's performance based on different key metrics, including accuracy and precision. |
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