A Framework on Performance Analysis of Mathematical Model-Based Classifiers in Detection of Epileptic Seizure from EEG Signals with Efficient Feature Selection

Abstract

Epilepsy is one of the neurological conditions that are diagnosed in the vast majority of patients. Electroencephalography (EEG) readings are the primary tool that is used in the process of diagnosing and analyzing epilepsy. The epileptic EEG data display the electrical activity of the neurons and provide a significant amount of knowledge on pathology and physiology. As a result of the significant amount of time that this method requires, several automated classification methods have been developed. In this paper, three wavelets such as Haar, dB4, and Sym 8 are employed to extract the features from A–E sets of the Bonn epilepsy dataset. To select the best features of epileptic seizures, a Particle Swarm Optimization (PSO) technique is applied. The extracted features are further classified using seven classifiers like linear regression, nonlinear regression, Gaussian Mixture Modeling (GMM), K-Nearest Neighbor (KNN), Support Vector Machine (SVM-linear), SVM (polynomial), and SVM Radial Basis Function (RBF). Classifier performances are analyzed through the benchmark parameters, such as sensitivity, specificity, accuracy, F1 Score, error rate, and g-means. The SVM classifier with RBF kernel in sym 8 wavelet features with PSO feature selection method attains a higher accuracy rate of 98% with an error rate of 2%. This classifier outperforms all other classifiers.

Survey Analysis on smart features selection for machine learning techniques mainly applied to EEG.

“…V. S. Hemachandira et al [21] one of the most common neurological conditions of individuals have is epilepsy. Moreover, Electroencephalography (EEG) is used to describe the process of diagnosing epilepsies.…”

An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy

“…In order to extract the features of EEG signals effectively, the decomposition of the signal is required to be performed first. Since the wavelet transform can handle non-smooth and complex signals such as EEG signals while the traditional Fourier transform used for time–frequency domain analysis of signals can only handle smooth signals, a large number of studies have employed Discrete Wavelet Transform (DWT) to decompose EEG signals [ 5 – 7 ]. Furthermore, analyzing and extracting the effective signal features play an important role in classification, to realize the automatic detection of epilepsy.…”