Abstract
The main objective of this paper is to diagnose epileptic seizures using diverse machine learning techniques. Since EEG signals are highly nonstationary, wavelet transformation is applied to raw EEG signals to extract spectral information in time–frequency domain. These spectral features are fed as input to machine learning techniques to train the model and to predict the epileptic seizures. Machine learning techniques such as Principal Component Analysis (PCA), Linear Regression (LR), Support Vector Machine (SVM), Linear SVC, k-Nearest Neighbor (kNN), and Naive Bayes are explored for comparison and to validate the results. Nave Bayes and SVM classifiers recorded high recognition accuracies of 95.86% and 98.30%, respectively, and has outperformed other ML techniques to detect epileptic seizures using wavelet features in EEG signals.
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Mahantesh, K., Chetana, R. (2020). Detection of Epileptic Seizures in EEG—Inspired by Machine Learning Techniques. In: Bhateja, V., Satapathy, S., Zhang, YD., Aradhya, V. (eds) Intelligent Computing and Communication. ICICC 2019. Advances in Intelligent Systems and Computing, vol 1034. Springer, Singapore. https://doi.org/10.1007/978-981-15-1084-7_42
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