Self-supervised learning for arrhythmia classification.
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Data
2023
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Resumo
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.
Descrição
Programa de Pós-Graduação em Ciência da Computação. Departamento de Ciência da Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto.
Palavras-chave
Deep learning, Arrhythmia detection, Self supervised learning, Electrocardiogram - ECG
Citação
SILVA, 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.