An Efficient Epileptic Seizure Detection Technique using Discrete Wavelet Transform and Machine Learning Classifiers. Issue 1 (1st July 2022)

• Record Type: Journal Article
• Authors: Guharoy, Rabel; Jana, Nanda Dulal; Biswas, Suparna
• Is Part Of: Journal of physics. Volume 2286:Issue 1(2022)
• Publication Date: 2022-07-01
• Subjects: Electroencephalography (EEG) -- Discrete wavelet transform (DWT) -- Principal Component Analysis (PCA) -- Machine learning classifiers; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2286/1/012013
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: This paper presents an epilepsy detection method based on discrete wavelet transform (DWT) with 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 main features in low dimensional feature space. Three classifiers name: Support Vector Machine (SVM) classifier, K-Nearest-Neighbor (KNN) classifier, and Naive Bayes (NB) classifier have been used in the proposed work for classifying the EEG signals. The raised method is tested over Bonn databases and provides a maximum of 100% recognition accuracy for KNN, SVM, NB classifiers.