Abstract
Epilepsy occurs when localized electrical activity of neurons suffer from an imbalance. One of the most adequate methods for diagnosing and monitoring is via the analysis of electroencephalographic (EEG) signals. Despite there is a wide range of alternatives to characterize and classify EEG signals for epilepsy analysis purposes, many key aspects related to accuracy and physiological interpretation are still considered as open issues. In this paper, this work performs an exploratory study in order to identify the most adequate frequently-used methods for characterizing and classifying epileptic seizures. In this regard, a comparative study is carried out on several subsets of features using four representative classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The framework uses a well-known epilepsy dataset and runs several experiments for two and three classification problems. The results suggest that DWT decomposition with SVM is the most suitable combination.
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Available on UCI machine learning repository https://archive.ics.uci.edu/ml.
References
Bajaj, V., Pachori, R.B.: EEG signal classification using empirical mode decomposition and support vector machine. In: Deep, K., Nagar, A., Pant, M., Bansal, J.C. (eds.) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. AISC, vol. 131, pp. 623–635. Springer, New Delhi (2012). https://doi.org/10.1007/978-81-322-0491-6_57
Christensen, J., Sidenius, P.: Epidemiology of epilepsy in adults: implementing the ILAE classification and terminology into population-based epidemiologic studies. Epilepsia 53, 14–17 (2012)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2000)
Hall, M.A.: Correlation-based feature subset selection for machine learning. Ph.D. thesis, University of Waikato, Hamilton, New Zealand (1999)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)
Ocak, H.: Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Systems with Applications 36(2), 2027–2036 (2009)
Qazi, K.I., Lam, H., Xiao, B., Ouyang, G., Yin, X.: Classification of epilepsy using computational intelligence techniques. CAAI Trans. Intell. Technol. 1(2), 137 – 149 (2016). https://doi.org/10.1016/j.trit.2016.08.001, http://www.sciencedirect.com/science/article/pii/S2468232216300142
Qiuyi, W., Ernest, F.: Epileptic Seizure Recognition Data Set. UCI Machine learning repository (2017). https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition
Stafstrom, C.E., Carmant, L.: Seizures and epilepsy: an overview for neuroscientists. Cold Spring Harbor Perspect. Med. 5(6), a022426 (2015). https://doi.org/10.1101/cshperspect.a022426
Tsipouras, M.G.: Spectral information of EEG signals with respect to epilepsy classification. EURASIP J. Adv. Sig. Process. 2019(1), 10 (2019). https://doi.org/10.1186/s13634-019-0606-8
Übeyli, E.D.: Statistics over features: EEG signals analysis. Comput. Biol. Med. 39(8), 733–741 (2009)
Wang, L., et al.: Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis, May 2017. https://www.mdpi.com/1099-4300/19/6/222
Zhou, M., et al.: Epileptic seizure detection based on EEG signals and CNN. Front. Neuroinformatics 12, 95–95 (2018). https://doi.org/10.3389/fninf.2018.00095, https://www.ncbi.nlm.nih.gov/pubmed/30618700, 30618700[pmid]
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Authors thank to the SDAS Research Group (www.sdas-group.com) for its valuable support.
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Vega-Gualán, E., Vargas, A., Becerra, M., Umaquinga, A., Riascos, J.A., Peluffo, D. (2019). Exploring the Characterization and Classification of EEG Signals for a Computer-Aided Epilepsy Diagnosis System. In: Liang, P., Goel, V., Shan, C. (eds) Brain Informatics. BI 2019. Lecture Notes in Computer Science(), vol 11976. Springer, Cham. https://doi.org/10.1007/978-3-030-37078-7_19
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