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- W4200354429 abstract "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." @default.
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- W4200354429 date "2022-11-01" @default.
- W4200354429 modified "2023-10-14" @default.
- W4200354429 title "Gradient boosting machines fusion for automatic epilepsy detection from EEG signals based on wavelet features" @default.
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- W4200354429 doi "https://doi.org/10.1016/j.jksuci.2021.11.015" @default.
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