Identifying epileptic EEGs and congestive heart failure ECGs under unified framework of wavelet scattering transform, bidirectional weighted (2D)²PCA and KELM

• Autorzy: Zhang Tao , Chen Wanzhong , Chen Xiaojuan

Identyfikatory

DOI

10.1016/j.bbe.2023.01.002

• Języki publikacji: EN

Abstrakty

EN

In order to achieve the accurate identifications of various electroencephalograms (EEGs) and electrocardiograms (ECGs), a unified framework of wavelet scattering transform (WST), bidirectional weighted two-directional two-dimensional principal component analysis (BW(2D)²PCA) and grey wolf optimization based kernel extreme learning machine (KELM) was put forward in this study. To extract more discriminating features in the WST domain, the BW(2D)²PCA was proposed based on original two-directional two-dimensional principal component analysis, by considering both the contribution of eigenvalue and the variation of two adjacent eigenvalues. Totally fifteen classification tasks of classifying normal vs interictal vs ictal EEGs, non-seizure vs seizure EEGs and normal vs congestive heart failure (CHF) ECGs were investigated. Applying patient non-specific strategy, the proposed scheme reported ACCs of no less than 99.300 ± 0.121 % for all the thirteen classification cases of Bonn dataset in classifying normal vs interictal vs ictal EEGs, MCC of 90.947 ± 0.128 % in distinguishing non-seizure vs seizure EEGs of CHB-MIT dataset, and MCC of 99.994 ± 0.001 % in identifying normal vs CHF ECGs of BBIH dataset. Experimental results indicate BW(2D)²PCA based framework outperforms (2D)²PCA based scheme, the high-performance results manifest the effectiveness of the proposed framework and our proposal is superior to most existing approaches.

Słowa kluczowe

EN

WST; wavelet scattering transform; BW(2D)2 PCA; grey wolf optimization; kernel extreme learning machine; KELM; EEG; electroencephalogram; ECG; electrocardiogram

PL

WST; transformata falkowa; BW(2D)2 PCA; optymalizacja szarego wilka; KELM; EEG; elektroencefalogram; EKG; elektrokardiogram

• 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. 1
• Strony: 279--297
• Opis fizyczny: Bibliogr. 89 poz., rys., tab., wykr.

Twórcy

autor

Zhang Tao

• College of Communication Engineering, Jilin University, China

autor

Chen Wanzhong

• College of Communication Engineering, Jilin University, No. 5988, Renmin Street, Changchun, Jilin Province, China

autor

Chen Xiaojuan

• FAW Tooling Die Manufacturing Company LTD, 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)