An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers

• URL: http://arxiv.org/abs/2109.13811v1
• Date: Sun, 26 Sep 2021 18:30:04 GMT
• Title: An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers
• Authors: Rabel Guharoy, Nanda Dulal Jana and Suparna Biswas
• Abstract summary: This paper presents an epilepsy detection method based on discrete wavelet transform (DWT) and Machine learning classifiers. DWT has been used for feature extraction as it provides a better decomposition of the signals in different frequency bands.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This paper presents an epilepsy detection method based on discrete wavelet transform (DWT) and Machine learning classifiers. Here DWT has been used for feature extraction as it provides a better decomposition of the signals in different frequency bands. At first, DWT has been applied to the EEG signal to extract the detail and approximate coefficients or different sub-bands. After the extraction of the coefficients, principal component analysis (PCA) has been applied on different sub-bands and then a feature level fusion technique is used to extract the important features in low dimensional feature space. Three classifiers namely: Support Vector Machine (SVM) classifier, K-Nearest-Neighbor (KNN) classifier, and Naive Bayes (NB) Classifiers have been used in the proposed work for classifying the EEG signals. The proposed method is tested on Bonn databases and provides a maximum of 100% recognition accuracy for KNN, SVM, NB classifiers.

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