Abstract
The seizure detection from an EEG signal has shown substantial potential for the improvement of accuracy and efficiency in epilepsy diagnosis. The success of the diagnosis is determined by the feature extraction step, which aims to identify meaningful patterns associated to various mental activity. During the ictal stage, which occurs during a seizure, the mental activities change. As a result, a wavelet-based strategy to extracting features from EEG data is proposed in this paper. By using wavelet analysis, the sub-bands can be obtained which corresponds to a certain frequency range. These frequency range are related to various mental activities. The EEG signal is decomposed into various sub-bands Delta, Theta, Alpha, Beta and Gamma. After that, the maximum energy and power are found for each sub-band which used as features in order to obtain descriptors. These descriptors are evaluated using different classifiers K-nearest neighbor, Quadratic Discriminant, Kernel Naïve Bayes, Gaussian support vector machine and Ensemble subspace KNN. The analysis showed that levels of Discrete Wavelet Transform and the use of time-frequency features affect the final seizure detection performance. The set A and set E from the open access dataset of Bonn University is used for testing and validation.
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Meshram, P.S., Gharpure, D.C. (2022). Epileptic Seizure Detection Using Wavelet-Based Features from Different Sub-bands. In: Pundir, A.K.S., Yadav, N., Sharma, H., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-1324-2_26
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DOI: https://doi.org/10.1007/978-981-19-1324-2_26
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