Um Sistema e-Health Inteligente para Análise e Detecção de Doenças Cardíacas
Resumo
A indústria de cuidados médicos vem evoluindo a sua prestação de serviços, focando na qualidade do atendimento ao paciente. Tem-se então a necessidade de uma infraestrutura para monitoramento da saúde dos pacientes a partir da análise dos dados destes pacientes, com intuito de detectar possíveis doenças, dentre elas, doenças cardíacas. Dentro deste contexto, este artigo apresenta um sistema e-Health inteligente para analisar os dados biológicos dos pacientes e detectar possíveis complicações cardíacas. No sistema proposto aplica-se três fases: (I) trata-se os dados através de filtros; (II) técnicas para extração das informações úteis; e, (III) os dados são analisados e classificados para detectar doenças cardíacas. Os experimentos realizados utilizando-se uma base dados real mostram que o sistema proposto é capaz de classificar com alta acurácia as anomalias cardíacas.
Palavras-chave:
Analise de Doenças, Healthcare
Referências
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Moody, G. B. and Mark, R. G. (2001). The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3):45-50.
Moreira, D. A., Chaves, L. G., Lima, B. A., Almeida, T. P., Gomes, R. L., Celestino, J., and Neto, A. J. (2018). Detecção de pontos fiduciais em eletrocardiogramas para sistemas de saúde. II Workshop de Computação Urbana (CoUrb), 2018, Campos do Jor-dão. Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC).
Oresko, J. J., Jin, Z., Cheng, J., Huang, S., Sun, Y., Duschl, H., and Cheng, A. C. (2010). A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Transactions on Information Technology in Biomedicine, 14(3):734-740.
Organization, W. H. et al. (2016). Hearts: technical package for cardiovascular disease management in primary health care.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011).
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Wong, T.-T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48(9):2839-2846.
Xu, K., Guo, S., Cao, N., Gotz, D., Xu, A., Qu, H., Yao, Z., and Chen, Y. (2018). Ec-glens: Interactive visual exploration of large scale ecg data for arrhythmia detection. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, page 663. ACM.
Zhao, R., Wang, D., Yan, R., Mao, K., Shen, F., and Wang, J. (2018). Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Transactions on Industrial Electronics, 65(2):1539-1548.
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., and Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE communications surveys & tutorials, 17(4):2347-2376.
Begg, R., Lai, D. T., and Palaniswami, M. (2007). Computational intelligence in biome-dical engineering. CRC Press.
Carrera, D., Rossi, B., Fragneto, P., and Boracchi, G. (2019). Online anomaly detection for long-term ecg
monitoring using wearable devices. Pattern Recognition, 88:482-492.
Elgendi, M. (2016). Terma framework for biomedical signal analysis: An economic-inspired approach. Biosensors, 6(4):55.
Hamilton, P. S. and Tompkins, W. J. (1986). Quantitative investigation of qrs detection rules using the mit/bih arrhythmia database. IEEE transactions on biomedical engine-ering, (12):1157-1165.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Kiranyaz, S., Ince, T., and Gabbouj, M. (2016). Real-time patient-specific ecg classifica-tion by 1-d convolutional neural networks. IEEE Transactions on Biomedical Engine-ering, 63(3):664-675.
Mendis, S., Puska, P., Norrving, B., Organization, W. H., et al. (2011). Global atlas on cardiovascular disease prevention and control. Geneva: World Health Organization.
Metshein, M., Annus, P., Land, R., Krivošei, A., Ojarand, J., Aabloo, A., and Min, M. (2017). Variation of cardiac and respiratory waveform on human thoraxin the case of inductive coupling. In EMBEC & NBC 2017, pages 671-674. Springer.
Moody, G. B. and Mark, R. G. (2001). The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3):45-50.
Moreira, D. A., Chaves, L. G., Lima, B. A., Almeida, T. P., Gomes, R. L., Celestino, J., and Neto, A. J. (2018). Detecção de pontos fiduciais em eletrocardiogramas para sistemas de saúde. II Workshop de Computação Urbana (CoUrb), 2018, Campos do Jor-dão. Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC).
Oresko, J. J., Jin, Z., Cheng, J., Huang, S., Sun, Y., Duschl, H., and Cheng, A. C. (2010). A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Transactions on Information Technology in Biomedicine, 14(3):734-740.
Organization, W. H. et al. (2016). Hearts: technical package for cardiovascular disease management in primary health care.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011).
Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830.
Wong, T.-T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48(9):2839-2846.
Xu, K., Guo, S., Cao, N., Gotz, D., Xu, A., Qu, H., Yao, Z., and Chen, Y. (2018). Ec-glens: Interactive visual exploration of large scale ecg data for arrhythmia detection. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, page 663. ACM.
Zhao, R., Wang, D., Yan, R., Mao, K., Shen, F., and Wang, J. (2018). Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Transactions on Industrial Electronics, 65(2):1539-1548.
Publicado
10/09/2019
Como Citar
MOREIRA, Diego Alysson Braga; CHAVES, Levy Gurgel; LOPES GOMES, Rafael ; CELESTINO JÚNIOR, Joaquim .
Um Sistema e-Health Inteligente para Análise e Detecção de Doenças Cardíacas. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 3. , 2019, Gramado.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 194-207.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2019.7478.
