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

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, applied to EEG signal extract detail approximate coefficients or sub-bands. After coefficients, principal component analysis (PCA) sub-bands then level fusion technique is important features low dimensional space. Three classifiers namely: Support Vector (SVM) classifier, K-Nearest-Neighbor (KNN) Naive Bayes (NB) Classifiers have proposed work classifying signals. The tested Bonn databases maximum 100% recognition accuracy KNN, SVM, NB