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.