Study of EEG Signal for Epilepsy Detection and Localization Using Bagged Tree and SVM Algorithms

Mostafa A Abd-Elbaset ,Sherif H El-Gohary

doi:10.34257/gjmrkvol19is7pg1

Abstract

Epilepsy is considered one of the common medical and social disorders with unique characteristics. EEG signal was used for the classification and detection of epilepsy. This study proposed epilepsy classification without signal decomposition, as well as other algorithms used for decomposing the EEG signal to sub-bands like discrete wavelet transform (DWT) and dual-tree complex wavelet transform (DT-CWT). Descriptive comparisons were done between results for EEG signals with/without decomposition. The proposed algorithm includes the study of the extracted features and using machine learning kernels as Support Vector Machine (SVM) and bagged tree to achieve the optimal values of (accuracy-specificity-sensitivity and execution time). Results show that adding the line length to the group of features, the accuracy increased to 99.4%. By employing decomposing the EEG signal, the accuracy could be raised to 99.875 % even after reducing the number of features to only three features. These features are line length, STD, and mean. This study proposed different algorithms with minimum features for epilepsy classification and localization with optimum execution time.

Highlights

Epilepsy is considered one of the common medical Multilayer Perceptron Neural network) [8,9]
This study proposed different algorithms with minimum features for epilepsy classification and localization with distinct technique was used for decomposition as DT-CWT which separates the real and imaginary parts of each signal and decomposes them by different filters
Seizure detection is a significant step for epilepsy classification

Summary

Introduction

Epilepsy is considered one of the common medical Multilayer Perceptron Neural network) [8,9]. Study proposed epilepsy classification without signal decomposition, as well as other algorithms used for decomposing the EEG signal to sub-bands like discrete wavelet transform (DWT) and dual-tree complex wavelet. By employing decomposing the EEG signal, the accuracy could be raised to 99.875 % even after reducing the number of features to only three features. These features are line length, STD, and mean. This study proposed different algorithms with minimum features for epilepsy classification and localization with distinct technique was used for decomposition as DT-CWT which separates the real and imaginary parts of each signal and decomposes them by different filters. The ease of use and noninvasive technique gives the power to EEG to be widely used to diagnose brain diseases such as (autismepilepsy-head injury-dementia-brain tumors, etc...)

Methods

Results

Conclusion