Cardiac Arrhythmia Detection in ECG Signals Using Graph Convolutional Network
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%.
Referências
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adam, M., Gertych, A., and San Tan, R. (2017). A deep convolutional neural network model to classify heartbeats. Computers in biology and medicine, 89:389-396.
Cohen, A. (1986). Biomedical signal processing. CRC press.
De Chazal, P., O'Dwyer, M., and Reilly, R. B. (2004). Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering, 51(7):1196-1206.
Defferrard, M., Bresson, X., and Vandergheynst, P. (2016). Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems, 29:3844-3852.
Garcia, G., Moreira, G., Menotti, D., and Luz, E. (2017). Inter-patient ECG heartbeat classification with temporal VCG optimized by PSO. Scientific Reports, 7(1):1-11. Publisher: Nature Publishing Group.
Hammad, M., Iliyasu, A. M., Subasi, A., Ho, E. S., and Abd El-Latif, A. A. (2020). A multitier deep learning model for arrhythmia detection. IEEE Transactions on Instrumentation and Measurement, 70:1-9.
Hammond, D. K., Vandergheynst, P., and Gribonval, R. (2011). Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis, 30(2):129-150.
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., and Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in am bulatory electrocardiograms using a deep neural network. Nature medicine, 25(1):65-69. Publisher: Nature Publishing Group.
Kachuee, M., Fazeli, S., and Sarrafzadeh, M. (2018). Ecg heartbeat classification: A deep transferable representation. In 2018 IEEE International Conference on Healthcare Informatics (ICHI), pages 443-444. IEEE.
Kipf, T. N. and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
Lin, C.-C. and Yang, C.-M. (2014). Heartbeat classification using normalized rr intervals and morphological features. Mathematical Problems in Engineering, 2014.
Llamedo, M. and Martínez, J. P. (2010). Heartbeat classification using feature selection driven by database generalization criteria. IEEE Transactions on Biomedical Engineering, 58(3):616-625.
Luz, E. J. d. S., Schwartz, W. R., Cámara-Chávez, G., and Menotti, D. (2016). Ecg-based heartbeat classification for arrhythmia detection: A survey. Computer methods and programs in biomedicine, 127:144-164.
Mousavi, S. and Afghah, F. (2019). Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1308-1312. IEEE.
Müller, M. (2007). Dynamic time warping. Information retrieval for music and motion, pages 69-84.
Organization, W. H. (2021). Cardiovascular diseases (cvds). https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
Queiroz, V., Luz, E., Moreira, G., Guarda, Á., and Menotti, D. (2015). Automatic cardiac arrhythmia detection and classification using vectorcardiograms and complex networks. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 5203-5206. IEEE.
Riedel, D. E., Venkatesh, S., and Liu, W. (2007). Threshold dynamic time warping for spatial activity recognition. International journal of information and systems sciences, 3(3):392-405.
Senin, P. (2008). Dynamic time warping algorithm review. Information and Computer Science Department University of Hawaii at Manoa Honolulu, USA, 855(1-23):40.
Cohen, A. (1986). Biomedical signal processing. CRC press.
De Chazal, P., O'Dwyer, M., and Reilly, R. B. (2004). Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering, 51(7):1196-1206.
Defferrard, M., Bresson, X., and Vandergheynst, P. (2016). Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems, 29:3844-3852.
Garcia, G., Moreira, G., Menotti, D., and Luz, E. (2017). Inter-patient ECG heartbeat classification with temporal VCG optimized by PSO. Scientific Reports, 7(1):1-11. Publisher: Nature Publishing Group.
Hammad, M., Iliyasu, A. M., Subasi, A., Ho, E. S., and Abd El-Latif, A. A. (2020). A multitier deep learning model for arrhythmia detection. IEEE Transactions on Instrumentation and Measurement, 70:1-9.
Hammond, D. K., Vandergheynst, P., and Gribonval, R. (2011). Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis, 30(2):129-150.
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., and Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in am bulatory electrocardiograms using a deep neural network. Nature medicine, 25(1):65-69. Publisher: Nature Publishing Group.
Kachuee, M., Fazeli, S., and Sarrafzadeh, M. (2018). Ecg heartbeat classification: A deep transferable representation. In 2018 IEEE International Conference on Healthcare Informatics (ICHI), pages 443-444. IEEE.
Kipf, T. N. and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
Lin, C.-C. and Yang, C.-M. (2014). Heartbeat classification using normalized rr intervals and morphological features. Mathematical Problems in Engineering, 2014.
Llamedo, M. and Martínez, J. P. (2010). Heartbeat classification using feature selection driven by database generalization criteria. IEEE Transactions on Biomedical Engineering, 58(3):616-625.
Luz, E. J. d. S., Schwartz, W. R., Cámara-Chávez, G., and Menotti, D. (2016). Ecg-based heartbeat classification for arrhythmia detection: A survey. Computer methods and programs in biomedicine, 127:144-164.
Mousavi, S. and Afghah, F. (2019). Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1308-1312. IEEE.
Müller, M. (2007). Dynamic time warping. Information retrieval for music and motion, pages 69-84.
Organization, W. H. (2021). Cardiovascular diseases (cvds). https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
Queiroz, V., Luz, E., Moreira, G., Guarda, Á., and Menotti, D. (2015). Automatic cardiac arrhythmia detection and classification using vectorcardiograms and complex networks. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 5203-5206. IEEE.
Riedel, D. E., Venkatesh, S., and Liu, W. (2007). Threshold dynamic time warping for spatial activity recognition. International journal of information and systems sciences, 3(3):392-405.
Senin, P. (2008). Dynamic time warping algorithm review. Information and Computer Science Department University of Hawaii at Manoa Honolulu, USA, 855(1-23):40.
Publicado
07/06/2022
Como Citar
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.
