A Method for Epileptic Seizure Detection in EEG Signals Based on Tunable Q-Factor Wavelet Transform Method Using Grasshopper Optimization Algorithm With Support Vector Machine Classifier

Aims: Epilepsy is a brain disorder disease that affects people’s quality of life. If it is detected at an early stage, seizures will not spread from the initial area. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. However, this method cannot diagnose the state of epileptic seizure precisely. With the help of the Computer-Aided Diagnosis (CAD) system, neurologists can diagnose epileptic seizure stages correctly. This study aims to present a novel method for epileptic seizures detection in EEG signals. Methods & Materials: The Bonn dataset was used in this study with avaibale EEG signals divided into 5-second windows. Then, the Tunable Q-Factor Wavelet Transform (TQWT) was utilized to decompose the segmented EEG signals into various sub-bands. Several statistical and nonlinear features based on fractal dimension and entropy algorithms were extracted from the TQWT sub-bands. Then, the Autoencoder (AE) method with 7 layers was applied to reduce the number of features. Finally, the Support Vector Machine (SVM) and Grasshopper Optimization Algorithm with SVM classifier (GOA/SVM) were used for their classification compared to the K-Nearest Neighbors and