Ferramenta Computacional para Classificação de ECG Humanos com Detecção de Defeitos no Eletrocardiógrafo
Resumo
Há muito tempo, os eletrocardiogramas (ECGs) vêm sendo utilizados para diagnosticar problemas cardíacos. No entanto, obter uma classificação automática satisfatória de ECGs em sistemas de e-Health, é uma tarefa desafiadora, devido a interferências operacionais e falhas às quais esses dispositivos estão submetidos. Neste artigo, apresentamos a ferramenta computacional Cyber-ECG para classificação automática de sinais de ECG, com detecção de defeitos de sensores do eletrocardiógrafo. A Cyber-ECG foi implementada em ambiente Simulink/MATLAB e avaliada a partir de séries temporais de ECGs disponíveis em banco de dados público. A ferramenta proposta obteve uma precisão de 84 % e 80 % ao classificar arritmias e batimentos normais, respectivamente. Para essas mesmas classes, os valores de F1-Score são 82 % e 83 %. Portanto, a ferramenta apresentou funcionamento satisfatório e, em alguns casos, teve desempenho superior em comparação com outros resultados de métodos de classificação de sinais de ECG relatados na literatura científica. O detector de defeito foi avaliado a partir de um módulo de injeção de falhas integrado à ferramenta Cyber-ECG, o que permitiu verificar a eficácia do método proposto.
Palavras-chave:
Eletrocardiogramas, Classificação de ECG, e-Health, Tolerância a Falhas
Referências
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M. U. Jakobsen, E. Trolle, M. Outzen, H. Mejborn, M. G. Grønberg, C. B. Lyndgaard, A. Stockmarr, S. K. Venø, and A. Bysted, “Intake of dairy products and associations with major atherosclerotic cardiovascular diseases: a systematic review and meta-analysis of cohort studies,” Scientific reports, vol. 11, no. 1, pp. 1–28, 2021.
G. Eysenbach, “What is e-health?,” Journal of medical Internet research, vol. 3, no. 2, p. e20, 2001.
F. Abdali-Mohammadi, V. Bajalan, and A. Fathi, “Toward a fault tolerant architecture for vital medical-based wearable computing,” Journal of medical systems, vol. 39, no. 12, p. 149, 2015.
S. R. Islam, D. Kwak, M. H. Kabir, M. Hossain, and K.-S. Kwak, “The internet of things for health care: a comprehensive survey,” IEEE Access, vol. 3, pp. 678–708, 2015.
R. Horst, D. Jewett, and D. Lenoski, “The risk of data corruption in microprocessor-based systems,” in Fault-Tolerant Computing, 1993. FTCS-23. Digest of Papers., the Twenty-Third International Symposium on, pp. 576–585, IEEE, 1993.
M. Schmidt and R. Obermaisser, “Adaptive and technology-independent architecture for fault-tolerant distributed aal solutions,” Computers in biology and medicine, 2017.
R. Sharifi and R. Langari, “Sensor fault diagnosis with a probabilistic decision process,” Mechanical systems and signal processing, vol. 34, no. 1-2, pp. 146–155, 2013.
T. N. Gia, A.-M. Rahmani, T. Westerlund, P. Liljeberg, and H. Tenhunen, “Fault tolerant and scalable iot-based architecture for health monitoring,” in 2015 IEEE Sensors Applications Symposium (SAS), pp. 1–6, IEEE, 2015.
C. C. Oliveira and J. M. Da Silva, “Fault diagnosis in highly dependable medical wearable systems,” Journal of Electronic Testing, vol. 32, no. 4, pp. 467–479, 2016.
D.-J. Kim and B. Prabhakaran, “Motion fault detection and isolation in body sensor networks,” Pervasive and Mobile Computing, vol. 7, no. 6, pp. 727–745, 2011.
M. Merah, A. Ouamri, A. Nait-Ali, and M. Keche, “Fault tolerant neural network for ecg signal classification systems,” Advances in Electrical and Computer Engineering, vol. 11, no. 3, pp. 17–24, 2011.
S. A. Kumar, B. Bhargava, R. Macêdo, and G. Mani, “Securing iot-based cyber-physical human systems against collaborative attacks,” in Internet of Things (ICIOT), 2017 IEEE International Congress on, pp. 9–16, IEEE, 2017.
J. Malmivuo and R. Plonsey, Bioelectromagnetism: principles and applications of bioelectric and biomagnetic fields. Oxford University Press, USA, 1995.
D. De Araujo, A. A. Carneiro, E. R. Moraes, and O. Baffa, “Biomagnetismo: nova interface entre a física e a biologia,” Ciência Hoje, pp. 24–29, 1999.
C. Chen, Z. Hua, R. Zhang, G. Liu, and W. Wen, “Automated arrhythmia classification based on a combination network of cnn and lstm,” Biomedical Signal Processing and Control, vol. 57, p. 101819, 2020.
V. Krasteva, I. Jekova, R. Leber, R. Schmid, and R. Abächerli, “Realtime arrhythmia detection with supplementary ecg quality and pulse wave monitoring for the reduction of false alarms in icus,” Physiological measurement, vol. 37, no. 8, p. 1273, 2016.
B. Vandendriessche, M. Abas, T. E. Dick, K. A. Loparo, and F. J. Jacono, “A framework for patient state tracking by classifying multiscalar physiologic waveform features,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 12, pp. 2890–2900, 2017.
Q. Li, C. Rajagopalan, and G. D. Clifford, “Ventricular fibrillation and tachycardia classification using a machine learning approach,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 6, pp. 1607–1613, 2013.
Z. I. Attia, S. Kapa, F. Lopez-Jimenez, P. M. McKie, D. J. Ladewig, G. Satam, P. A. Pellikka, M. Enriquez-Sarano, P. A. Noseworthy, T. M. Munger, et al., “Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram,” Nature medicine, vol. 25, no. 1, p. 70, 2019.
J. Pan and W. J. Tompkins, “A real-time qrs detection algorithm,” IEEE transactions on biomedical engineering, no. 3, pp. 230–236, 1985.
A. Avizienis, J.-C. Laprie, B. Randell, and C. Landwehr, “Basic concepts and taxonomy of dependable and secure computing,” IEEE transactions on dependable and secure computing, vol. 1, no. 1, pp. 11–33, 2004.
R. J. A. Macêdo and J. A. Farines, “Projeto de sistemas distribuídos e de tempo real para automação. edufba, 2018, 250p,” 2018.
C. Pastore, C. Pinho, H. Germiniani, N. Samesima, and R. Mano, “Diretrizes da sociedade brasileira de cardiologia sobre análise e emissão de laudos eletrocardiográficos,” Arquivos Brasileiros de Cardiologia, vol. 93, no. 3, pp. 1–19, 2009.
P. S. Hamilton, “Open source ecg analysis software documentation,” Computers in cardiology, vol. 2002, pp. 101–104, 2002.
L. Chhabra, N. Goel, L. Prajapat, D. H. Spodick, and S. Goyal, “Mouse heart rate in a human: diagnostic mystery of an extreme tachyarrhythmia,” Indian pacing and electrophysiology journal, vol. 12, no. 1, pp. 32–35, 2012.
S. M. Al-Khatib, W. G. Stevenson, M. J. Ackerman, W. J. Bryant, D. J. Callans, A. B. Curtis, B. J. Deal, T. Dickfeld, M. E. Field, G. C. Fonarow, A. M. Gillis, C. B. Granger, S. C. Hammill, M. A. Hlatky, J. A. Joglar, G. N. Kay, D. D. Matlock, R. J. Myerburg, and R. L. Page, “2017 aha/acc/hrs guideline for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death,” Circulation, vol. 138, no. 13, pp. e272–e391, 2018.
A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,” circulation, vol. 101, no. 23, pp. e215–e220, 2000.
M. PhysioNet, “Arrhythmia database,” URL: https://www.physionet.org/physiobank/database/mitdb, 2017.
A. Goldberger, L. Amaral, L. Glass, J. Hausdorff, P. Ivanov, R. Mark, J. Mietus, G. GB, C. Peng, and H. Stanley, “The mit-bih normal sinus rhythm database,” Circulation, vol. 101, no. 23, pp. e215–e220.
AAMI et al., “American national standard for ambulatory electrocardiographs, publication ansi,” AAMI EC38-1994, 1994.
E. E. A. Hussein et al., Fault Tolerance Analysis in Fuel Control System Using SIMULINK Software. PhD thesis, Sudan University of Science & Technology, 2016.
L. T. Patrick E. McSharry, Gari D. Clifford and L. A. Smith, “A dynamical model for generating synthetic electrocardiogram signals,” IEEE Transactions On Biomedical Engineering, vol. 50, no. 3, pp. 289– 294, 2003.
S. Tuli, N. Basumatary, S. S. Gill, M. Kahani, R. C. Arya, G. S. Wander, and R. Buyya, “Healthfog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments,” Future Generation Computer Systems, vol. 104, pp. 187–200, 2020.
Q. Yao, R. Wang, X. Fan, J. Liu, and Y. Li, “Multi-class arrhythmia detection from 12-lead varied-length ecg using attention-based timeincremental convolutional neural network,” Information Fusion, vol. 53, pp. 174–182, 2020.
J. Chu, H. Wang, and W. Lu, “A novel two-lead arrhythmia classification system based on cnn and lstm,” Journal of Mechanics in Medicine and Biology, vol. 19, no. 03, p. 1950004, 2019.
V. K. Varadana, P. Raib, S. C. Ohb, and P. S. Kumarb, “Wearable technology and mobile platform for human health monitoring,” in Forum Electromagn. Res. Methods Appl. Technol, vol. 16, pp. 1–38, 2016.
T. M. Inc., “Signal classification using waveletbased features and support vector machines,” URL: [link], 2020.
Publicado
22/11/2021
Como Citar
RIBEIRO, Maycon E. F.; CRUZ, Juan A. L.; MACÊDO, Raimundo J. de A..
Ferramenta Computacional para Classificação de ECG Humanos com Detecção de Defeitos no Eletrocardiógrafo. In: ARTIGOS COMPLETOS - SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 11. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 72-79.
ISSN 2763-9002.
DOI: https://doi.org/10.5753/sbesc_estendido.2021.18496.
