Cardiac Arrhythmia Detection in ECG Signals Using Graph Convolutional Network

  • Guilherme Silva UFOP
  • Pedro Silva UFOP
  • Vander L. S. Freitas UFOP
  • Gladston J. P. Moreira UFOP
  • Eduardo Luz UFOP

Resumo


Segundo a Organização Mundial da Saúde, até 2030, 23,6 milhões de pessoas morrerão de doenças cardíacas. Portanto, a detecção automática de arritmia é desejável. As técnicas baseadas em redes neurais têm obtido ótimos resultados para este problema. Este trabalho visa explorar a detecção de arritmias com Rede Convolucional de Grafos e Dynamic Time Warping para alinhar os batimentos cardíacos. Até onde sabemos, este é o primeiro trabalho a abordar o problema como um único grafo com os batimentos cardíacos como nós. Os resultados indicam que a abordagem é promissora com Predição Positiva de 100% para detecção de batimentos ectópicos supraventriculares e Sensibilidade de 100% para detecção de batimentos ectópicos ventriculares com acurácia global de 90%.

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Publicado
07/06/2022
SILVA, Guilherme; SILVA, Pedro; FREITAS, Vander L. S.; MOREIRA, Gladston J. P.; LUZ, Eduardo. Cardiac Arrhythmia Detection in ECG Signals Using Graph Convolutional Network. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. , 2022, Teresina. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 25-35. ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2022.222434.

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