Feature selection for ECG classification: analysis of a new method based on diversity in visibility graphs
Abstract
Here, we propose an innovative approach for feature selection in electrocardiogram classification, employing visibility graphs and a diversity metric. The methodology is evaluated through a classification pipeline, comparing the effectiveness of feature selection with random choices. Preliminary results are shown.References
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Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al. (2015). Xgboost: extreme gradient boosting. R package version 0.4-2, 1(4):1–4.
Dempster, A., Petitjean, F., and Webb, G. I. (2020). Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery, 34(5):1454–1495.
Holme, P. and Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3):97–125. Temporal Networks.
Lacasa, L., Luque, B., Ballesteros, F., Luque, J., and Nuno, J. C. (2008). From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences, 105(13):4972–4975.
Oliveira, R., Freitas, V., Moreira, G., and Luz, E. (2022). Explorando redes neurais de grafos para classificação de arritmias. In Proceedings of the 22nd Brazilian Symposium on Computing Applied to Health, pages 178–189, Porto Alegre, RS, Brasil. SBC.
Ribeiro, A. H., Paixao, G. M., Lima, E. M., Horta Ribeiro, M., Pinto Filho, M. M., Gomes, P. R., Oliveira, D. M., Meira Jr, W., Schon, T. B., and Ribeiro, A. L. P. (2021). CODE-15%: a large scale annotated dataset of 12-lead ECGs.
Ribeiro, A. H., Ribeiro, M. H., Paixão, G. M., Oliveira, D. M., Gomes, P. R., Canazart, J. A., Ferreira, M. P., Andersson, C. R., Macfarlane, P. W., Meira Jr, W., et al. (2020). Automatic diagnosis of the 12-lead ecg using a deep neural network. Nature communications, 11(1):1–9.
Published
2024-04-03
How to Cite
COELHO, Paulo; SALIBA, Samir; RAMOS, Luís; VIMIEIRO, Renato.
Feature selection for ECG classification: analysis of a new method based on diversity in visibility graphs. In: REGIONAL SCHOOL OF APPLIED COMPUTING FOR HEALTH (ERCAS), 9. , 2024, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 49-52.
DOI: https://doi.org/10.5753/ercas.2024.238705.
