Luz, Eduardo José da SilvaSilva, Pedro Henrique LopesSilva, Guilherme Augusto Lopes2023-11-132023-11-132023SILVA, Guilherme Augusto Lopes. Self-supervised learning for arrhythmia classification. 2023. 71 f. Dissertação (Mestrado em Ciência da Computação) - Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, 2023.http://www.repositorio.ufop.br/jspui/handle/123456789/17740Programa de Pós-Graduação em Ciência da Computação. Departamento de Ciência da Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto.Arrhythmias, heart diseases that are commonly diagnosed through electrocar- diograms (ECG), require computational methods for detection and classification to improve the physician’s diagnosis. Although there is abundant literature on the subject, the high intra-patient variability and noise of ECG signals pose challenges in developing practical machine-learning models. To address this, we propose a cus- tomized adjustment of machine learning models through self-supervised learning with human-in-the-loop. Our approach introduces a pretext task called ECGWavePuzzle, which improves classification performance through better generalization. Evaluation metrics on the MIT-BIH database demonstrate the effectiveness of our approach, which improved the ECGnet global accuracy by over 10% and the Mousavi’s CNN by over 13%. Additionally, the experimental results demonstrated that the proposed approach improved the sensitivity and positive predictive value of the arrhythmic classes for certain patients.pt-BRabertohttp://creativecommons.org/licenses/by/3.0/us/Deep learningArrhythmia detectionSelf supervised learningElectrocardiogram - ECGDissertacaoAutorização concedida ao Repositório Institucional da UFOP pelo(a) autor(a) em 09/11/2023 com as seguintes condições: disponível sob Licença Creative Commons 4.0 que permite copiar, distribuir e transmitir o trabalho, desde que sejam citados o autor e o licenciante.