Enhanced Gradient Boosting Machines Fusion based on the Pattern of Majority Voting for Automatic Epilepsy Detection

Abstract

Automatic detection of epilepsy based on EEG signals is one of the interesting fields to be developed in medicine to provide an alternative method for detecting epilepsy. High accuracy values are very important for accurate diagnosis in detecting epilepsy and avoid errors in diagnosing patients. Therefore, this study proposes the Enhanced Gradient Boosting Machines Fusion (Enhanced GBM Fusion) for automatically detecting epilepsy based on electroencephalographic (EEG) signals. Enhanced part of GBM Fusion is the pattern of majority voting evaluation based on the fusion of five-class and two-class GBM, called Enhanced GBM Fusion. The raw signal is extracted using Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT), then feature is selected by using Genetic Algorithm (GA) before classification. This proposed method was evaluated using five classes (normal in open eyes, normal in close eyes, interictal with hippocampal, interictal, and ictal) from the University of Bonn. The experimental results show that the proposed Enhanced GBM Fusion can increase the accuracy of GBM Fusion of 99.8% to classify five classes of epilepsy based on EEG signal. However, the performance of Enhanced GBM Fusion cannot be generalized to other datasets.