Abstract
Epilepsy is the key concern of medical practitioners and machine learning researchers since last decade. EEG signals play a very crucial role in early detection of epilepsy as well as cure of epilepsy. The traditional approach to analyze EEG signals includes two main steps: feature extraction and classification. Since multi-channel EEG data is chaotic data, selecting optimal features and classifying them are major challenges. There exist a number of feature extraction and classification techniques proposed by researchers which perform well. For feature extraction, wavelets have been proved to perform state-of-the-art performance, but no such state-of-the art performance exists for classification techniques. The classifiers explored in the presented work include random forest classifier, support vector machine, Naïve Bayes, k-nearest neighbor, decision trees, artificial neural network, and logistic regression. Experimental results on the UCI dataset represent that random forest is performing best with 99.78% accuracy.
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Raut, S., Rathee, N. (2021). Comparative Study on Machine Learning Classifiers for Epileptic Seizure Detection in Reference to EEG Signals. In: Bansal, P., Tushir, M., Balas, V., Srivastava, R. (eds) Proceedings of International Conference on Artificial Intelligence and Applications. Advances in Intelligent Systems and Computing, vol 1164. Springer, Singapore. https://doi.org/10.1007/978-981-15-4992-2_18
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DOI: https://doi.org/10.1007/978-981-15-4992-2_18
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