Wavelet-Hilbert transform based bidirectional least squares grey transform and modified binary grey wolf optimization for the identification of epileptic EEGs

• Autorzy: Liu Chang , Chen Wanzhong , Zhang Tao

Identyfikatory

DOI

10.1016/j.bbe.2023.04.003

• Języki publikacji: EN

Abstrakty

EN

Wavelet based seizure detection is an importance topic for epilepsy diagnosis via electroencephalogram (EEG), but its performance is closely related to the choice of wavelet bases. To overcome this issue, a fusion method of wavelet packet transformation (WPT), Hilbert transform based bidirectional least squares grey transform (HTBiLSGT), modified binary grey wolf optimization (MBGWO) and fuzzy K-Nearest Neighbor (FKNN) was proposed. The HTBiLSGTwas first proposed to model the envelope change of a signal, then WPT based HTBiLSGT was developed for EEG feature extraction by performing HTBiLSGT for each subband of each wavelet level. To select discriminative features, MBGWO was further put forward and employed to conduct feature selection, and the selected features were finally fed into FKNN for classification. The Bonn and CHB-MIT EEG datasets were used to verify the effectiveness of the proposed technique. Experimental results indicate the proposed WPT based HTBiLSGT, MBGWO and FKNN can respectively lead to the highest accuracies of 100% and 98.60 ± 1.35% for the ternary and quinary classification cases of Bonn dataset, it also results in the overall accuracy of 99.48 ± 0.61 for the CHB-MIT dataset, and the proposal is proven to be insensitive to the choice of wavelet bases.

Słowa kluczowe

EN

seizure detection; wavelet packet transformation; WPT; HTBiLSGT; MBGWO; FKNN

PL

wykrywanie napadu; transformata falkowa; WPT; HTBiLSGT; MBGWO; FKNN

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 2
• Strony: 442--462
• Opis fizyczny: Bibliogr. 78 poz., rys., tab., wykr.

Twórcy

autor

Liu Chang

• College of Communication Engineering, Jilin University, Changchun, China
• School of Medical Information, Changchun University of Chinese Medicine, China

autor

Chen Wanzhong

• College of Communication Engineering, Jilin University, Changchun, China

autor

Zhang Tao

• College of Communication Engineering, Jilin University, Changchun 130025, China

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)