Classification of patient by analyzing EEG signal using DWT and least square support vector machine

Abstract

Epilepsy is a neurological disorder which is most widespread in human DWT decomposes the signal into approximation and detail coefficients, the ApEn values the coefficients were computed using pattern length (m= 2 and 3) as an input feature for the Least square support vector machine (LS-SVM). The classification is done using LS-SVM and the resultswere compared using RBF and linear kernels. The proposed model has used the EEG data consisting of 5 classes and compared with using the approximate and detailed coefficients combined and individually. The classification accuracy of the LS-SVM using the RBF and Linear kernel with ApEn using different cases is compared and it is found that the best accuracy percentage is 100% with RBF kernel.

A Review of EEG Signal Analysis for Diagnosis of Neurological Disorders using Machine Learning

“…A variety of machine learning algorithms are used for the diagnosis of neurological disorders. The machine learning classifiers such as supervised, unsupervised, deep learning neural architectures and ensemble learning models are used for classification purposes in various EEG research studies [8,[20][21][22][23]. Some popular classifiers include LDA, SVM with linear and RBF kernel function, KNN, CNN, etc.…”

Human Emotion Recognition Based on EEG Signal Using Fast Fourier Transform and K-Nearest Neighbor

“…ISSN: 2415-6698 [11,12]. Conducting the introduction of the EEG signal requires a pattern of brain activity that is prominent and constant.…”

EEG Signal Classification using LDA and MLP Classifier

“…Recently, seizure prediction was extremely investigated through different models of automatic EEG signals classification and seizure detection [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. These models generally include three steps and widely vary in their theoretical approaches of the problem, validation of results and the amount of data analyzed.…”

“…In early attempts to show a relationship between the raw EEG and behavior, the first step is generally related to the decomposition of the raw data and the feature extraction processes. The decomposition process is applied in order to explore the raw data in frequency domain [19,23], in time-frequency domain [3,10,15,16,18,22], and in multi-domain [8,12,19,23]. After the decomposition process, statistic features and entropies features [6, 10,14,[20] were widely extracted.…”

“…Then, at the last step, these most prominent and discriminative features are used as inputs of a kernel machine for the classification decision. For this purpose, two mostly used kernel machines with different architectures were proposed, such as artificial neural network (ANN) [3,7,9,11,13,21] and support vector machine (SVM) [6,14,17,22]. Most of these studies focus on epilepsy by using a feature vector with at least three dimensions as inputs of a complex kernel machine.…”