Gradient boosting machines fusion for automatic epilepsy detection from EEG signals based on wavelet features

Automatic epilepsy detection from electroencephalogram (EEG) signals is an alternative to manual detection performed by a human expert. High classification performance is needed in automatic epilepsy detection from EEG signals to avoid miss detection. This study aims to propose a classification method for automatic epilepsy detection from EEG signals. The original EEG signals were processed using discrete Fourier transform (DFT) and discrete wavelet transform (DWT) prior to feature extraction. A fusion of 2-class and 3 class gradient boosting machines (GBM), called GBMs fusion, was used to classify EEG signals based on some statistical features and crossing frequency features. In addition, a genetic algorithm was used to select the prominent features before classification. The proposed method has been evaluated using three classes EEG signals (normal-interictal-ictal) included in EEG dataset from University of Bonn. The experimental result shows that the proposed GBMs fusion can improve the performance of a single GBM in classifying EEG signals. Furthermore, the proposed GBMs fusion can perfectly detect epilepsy from EEG signals with an accuracy of 100%. However, the performance of GBMs fusion may not be generalized to the other EEG dataset. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This study proposes a classification method for automatic epilepsy detection from EEG signals. The method processes the original signals using DFT and DWT, and classifies the signals using GBMs fusion. A genetic algorithm is utilized to select important features. The experimental results demonstrate that the proposed GBMs fusion improves the classification performance and achieves perfect epilepsy detection.