Complex-valued distribution entropy and its application for seizure detection

• Autorzy: Zhang Tao , Chen Wanzhong , Li Mingyang

Identyfikatory

DOI

10.1016/j.bbe.2019.10.006

• Języki publikacji: EN

Abstrakty

EN

Embedding entropies are powerful indicators in quantifying the complexity of signal, but most of them are only applicable for real-valued signal and the phase information is ignored if the analyzed signal is complex-valued. To assess the complexity of complex-valued signal, a new entropy called complex-valued distribution entropy (CVDistEn) was first proposed in this study. Two rules, namely equal width criterion and equal area criterion, were employed to demarcate the complex-valued space and two kinds of CVDistEn, i.e., CVDistEn1 and CVDistEn2 were raised. Furthermore, two novel feature extraction methods: (1) flexible analytic wavelet transform (FAWT)-based CVDistEn1 and logarithmic energy (LE) (FAWTC1L), (2) FAWT-based CVDistEn2 and LE (FAWTC2L) were subsequently put forward to characterize the interictal and ictal EEGs. Fuzzy k-nearest neighbors (FKNN) classifier was finally employed to classify these two types of EEGs automatically. Experiment results show the fusion method of FAWTC1L and FKNN leads to the best accuracies (ACCs)/Matthews correlation coefficients (MCCs) of 99.99%/99.97% and 100%/100% for Bonn and Neurology & Sleep Centre EEG datasets, respectively, while the other fusion scheme of FAWTC2L and FKNN results in the highest ACCs/MCCs of 99.97%/99.93% and 99.94%/99.89% for the same datasets. The proposed methods outperform other entropy-related seizure detection schemes and most of state-of-the-art techniques, they provide another new way for automated seizure detection in EEG.

Słowa kluczowe

EN

embedding entropies; complex valued distribution entropy; flexible analytic wavelet transform; logarithmic energy; fuzzy k-nearest neighbors; automated seizure detection

PL

transformata falkowa; energia logarytmiczna; metoda k najbliższych sąsiadów; wykrywanie napadu

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 1
• Strony: 306--323
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr.

Twórcy

autor

Zhang Tao

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

autor

Chen Wanzhong

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

autor

Li Mingyang

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

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).