Classificação de Estados Epilépticos em Sinais de EEG utilizando Detecção de Anomalias
Resumo
A epilepsia é um distúrbio neurológico caracterizado por uma perturbação elétrica anormal no cérebro, causando convulsões recorrentes. O exame mais utilizado no diagnóstico da epilepsia é o eletroencefalograma (EEG), onde a atividade elétrica cerebral de um paciente é mensurada e analisada visualmente. Contudo, identificar os padrões epilépticos no sinal de EEG através de inspeção visual é uma tarefa demorada e exaustiva para profissionais da área. Assim, o desenvolvimento de algoritmos que possam identificar esses padrões de forma automática, auxiliando o diagnóstico médico, tornou-se um importante desafio. Neste trabalho, propomos três modelos de classificação, baseados em detecção de anomalias. Os resultados obtidos demonstram alto desempenho e robustez a ruídos em relação resultados encontrados na literatura.
Palavras-chave:
Classificação de Estados Epilépticos, padrões Epilépticos, sinal EEG, detecção de anomalias
Referências
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MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proc. Fifth Berkeley Symp. Math. Stat. Probab. Vol. 1 Stat., pages 281–297, Berkeley, Calif. University of California Press.
McEwen, J. A. and Anderson, G. B. (1975). Modeling the stationarity and gaussianity of spontaneous electroencephalographic activity. IEEE Transactions on Biomedical Engineering, BME-22(5):361–369. DOI: https://doi.org/10.1109/TBME.1975.324504
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O’Shaughnessy, D. (1988). Linear predictive coding. IEEE Potentials, 7(1):29–32. DOI: https://doi.org/10.1109/45.1890
Sanei, S. and Chambers, J. A. (2007). EEG Signal Processing. England: John Wiley and Sons.
Shoeb, A. and Guttag, J. (2010). Application of Machine Learning To Epileptic Seizure Detection. Appearing in Proceedings of the 27th International Conference on Machine Learning , Haifa, Israel.
Subasi, A. and Ercebeli, E. (2005). Classification of EEG signal using neural network an logistic regression. Computer Methodis and Programs in Biomedicine, 78. pp. 87-99. DOI: https://doi.org/10.1016/j.cmpb.2004.10.009
Varun Chandola, Arindam Banerjee, V. K. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3). DOI: https://doi.org/10.1145/1541880.1541882
Webb, A. (2002). Statistical Pattern Recognition. John Wiley Sons. DOI: https://doi.org/10.1002/0470854774
World Health Organization (2017). Epilepsy fact sheet.
Acharya, U. R., Sree, S. V., Chattopadhyay, S., YU, W., and ANG, P. C. A. (2011). Application of recurrence quantification analysis for the automated identification of epileptic eeg signals. International Journal of Neural Systems, 21(03):199–211. PMID: 21656923. DOI: https://doi.org/10.1142/S0129065711002808
Adeli, H. and Ghosh-Dastidar, S. (2010). AUTOMATED EEG-BASED DIAGNOSIS OF NEUROLOGICAL DISORDERS: Inventing the Future of Neurology. New York: CRC Press. DOI: https://doi.org/10.1201/9781439815328
Bhattacharyya, A. and Pachori, R. B. (2017). A multivariate approach for patient-specific eeg seizure detection using empirical wavelet transform. IEEE Transactions on Biomedical Engineering, 64(9):2003–2015. DOI: https://doi.org/10.1109/TBME.2017.2650259
Chan, A. M., Sun, F. T., Boto, E. H., and Wingeier, B. M. (2008). Automated Seizure onset detection for accurate onset time determination in intracranial EEG. Clinical Neurophysiology, 119. pp. 2687-2696. DOI: https://doi.org/10.1016/j.clinph.2008.08.025
Davies, D. L. and Bouldin, D.W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI- 1(2):224–227. DOI: https://doi.org/10.1109/TPAMI.1979.4766909
Fergus, P., Hussain, A., Hignett, D., Al-Jumeily, D., Abdel-Aziz, K., and Hamdan, H. (2016). A machine learning system for automated whole-brain seizure detection. Applied Computing and Informatics, 12(1):70 – 89. DOI: https://doi.org/10.1016/j.aci.2015.01.001
Hayes, M. H. (1996). Statistical Digital Signal Processing and Modeling. USA: John Wiley and Sons.
Kanashiro, A. L. A. N. (2006). EPILEPSIA: prevalência, características epidemiológicas e lacuna de tratamento farmacológico. . 2006. 135 f. Master’s thesis, Tese (Faculdade de Ciências Médicas da Universidade Estadual de Campinas).
Khan, Y. U., Rafiuddin, N., and Farooq, O. (2012). Automated seizure detection in scalp eeg using multiple wavelet scales. In 2012 IEEE International Conference on Signal Processing, Computing and Control, pages 1–5. DOI: https://doi.org/10.1109/ISPCC.2012.6224361
Knorr, E.; NG, R. T. T. V. (2000). Distance-based outliers: Algorithms and applications. VLDB Journal, 8(3):237–253. DOI: https://doi.org/10.1007/s007780050
Liang, W., Pei, H., Cai, Q., and Wang, Y. (2019). Scalp eeg epileptogenic zone recognition and localization based on long-term recurrent convolutional network. Neurocomputing. DOI: https://doi.org/10.1016/j.neucom.2018.10.108
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proc. Fifth Berkeley Symp. Math. Stat. Probab. Vol. 1 Stat., pages 281–297, Berkeley, Calif. University of California Press.
McEwen, J. A. and Anderson, G. B. (1975). Modeling the stationarity and gaussianity of spontaneous electroencephalographic activity. IEEE Transactions on Biomedical Engineering, BME-22(5):361–369. DOI: https://doi.org/10.1109/TBME.1975.324504
Niedermeyer, E. and da Silva, F. L. (2001). Electroencephalography – Basic Principles, Clinical Applications and Related Fields, volume 1. Williams Williams. DOI: https://doi.org/10.1093/med/9780190228484.001.0001
O’Shaughnessy, D. (1988). Linear predictive coding. IEEE Potentials, 7(1):29–32. DOI: https://doi.org/10.1109/45.1890
Sanei, S. and Chambers, J. A. (2007). EEG Signal Processing. England: John Wiley and Sons.
Shoeb, A. and Guttag, J. (2010). Application of Machine Learning To Epileptic Seizure Detection. Appearing in Proceedings of the 27th International Conference on Machine Learning , Haifa, Israel.
Subasi, A. and Ercebeli, E. (2005). Classification of EEG signal using neural network an logistic regression. Computer Methodis and Programs in Biomedicine, 78. pp. 87-99. DOI: https://doi.org/10.1016/j.cmpb.2004.10.009
Varun Chandola, Arindam Banerjee, V. K. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3). DOI: https://doi.org/10.1145/1541880.1541882
Webb, A. (2002). Statistical Pattern Recognition. John Wiley Sons. DOI: https://doi.org/10.1002/0470854774
World Health Organization (2017). Epilepsy fact sheet.
Publicado
07/10/2019
Como Citar
CABRAL, Lucas; BARRETO, Guilherme A.; MONTEIRO, José Maria.
Classificação de Estados Epilépticos em Sinais de EEG utilizando Detecção de Anomalias. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 34. , 2019, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 145-156.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2019.8815.
