Classification of Epileptic States in EEG Signals Using Anomaly Detection
Abstract
Epilepsy is a neurological disorder characterized by an abnormal electrical disturbance in the brain, causing recurrent seizures. The most commonly used exam to diagnose epilepsy it the electroencephalogram (EEG), where a patient’s brain electrical activity is measured and visually analyzed. However, identifying epileptic patterns in the EEG signal through visual inspection is a time-consuming and exhaustive task for professionals in the field, motivating the development of algorithms that can identify these patterns, aiding the medical diagnosis. In this work, we propose three models based on anomaly detection. The results obtained demonstrate high performance and noise robustness in relation to results found in the literature.
Keywords:
Epileptics States classification, Epileptic patterns, EEG signal, Anomaly Detection
References
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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.
Published
2019-10-07
How to Cite
CABRAL, Lucas; BARRETO, Guilherme A.; MONTEIRO, José Maria.
Classification of Epileptic States in EEG Signals Using Anomaly Detection. In: BRAZILIAN SYMPOSIUM ON DATABASES (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.
