Um Método Ensemble para Classificação de Arritmias: Uma Avaliação Com Mais de 10 Mil Registros de Sinais de ECG

• Rafael F. Oliveira UFOP
• Anderson A. Ferreira UFOP
• Gladston J. P. Moreira UFOP
• Eduardo J. S. Luz UFOP

DOI: https://doi.org/10.5753/sbcas.2022.222429

Abstract

Early diagnosis of arrhythmias is of paramount importance since heart diseases cause so many deaths on the planet, according to the WHO. Therefore, automation of arrhythmia events is desirable. In this context, an automatic arrhythmia classification model is proposed based on voting ensemble, Discrete Wavelet Transform (DWT), and electrocardiogram (ECG). The model was evaluated in a large dataset with over 10,000 patients, under the inter-patient paradigm, and it achieved an average F1-score of 0.93, an efficiency increase of 2.15% over Random Forest and 1.07% over GradientBoost and XGradient Boostan. Thus, the proposed model has great potential for real use due to its robustness, and generalization power.

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