Generalized Stockwell transform and SVD-based epileptic seizure detection in EEG using random forest

Zhang, T.; Chen, W.; Li, M.

doi:10.1016/j.bbe.2018.03.007

Artykuł - szczegóły

Tytuł artykułu

Generalized Stockwell transform and SVD-based epileptic seizure detection in EEG using random forest

Autorzy

Zhang T. , Chen W. , Li M.

Wybrane pełne teksty z tego czasopisma

Identyfikatory

DOI

10.1016/j.bbe.2018.03.007

Warianty tytułu

Języki publikacji

Abstrakty

Purpose: Visual inspection of electroencephalogram (EEG) records by neurologist is the main diagnostic method of epilepsy but it is particularly time-consuming and expensive. Hence, it is of great significance to develop automatic seizure detection technique. Methods: In this work, a seizure detection approach, synthesizing generalized Stockwell transform (GST), singular value decomposition (SVD) and random forest, was proposed. Utilizing GST, the raw EEG was transformed into a time–frequency matrix, then the global and local singular values were extracted by SVD from the holistic and partitioned matrices of GST, respectively. Subsequently, four local parameters were calculated from each vector of local singular values. Finally, the global singular value vectors and local parameters were respectively fed into two random forest classifiers for classification, and the final category of a testing EEG was voted based on sub-labels obtained from the trained classifiers. Results: Four most common but challenging classification tasks of Bonn EEG database were investigated. The highest accuracies of 99.12%, 99.63%, 99.03% and 98.62% were achieved using our presented technique, respectively. Conclusions: Our proposed technique is comparable or superior to other up-to-date methods. The presented method is promising and able to handle with kinds of epileptic seizure detection tasks with satisfactory accuracy.

Słowa kluczowe

electroencephalogram automatic seizure detection generalized Stockwell transform singular value decomposition random forest

elektroencefalogram napad padaczkowy transformacja Stockwella rozkład na wartości szczególne las losowy

Wydawca

Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Elsevier

Czasopismo

Biocybernetics and Biomedical Engineering

Rocznik

2018

Tom

Vol. 38, no. 3

Strony

519--534

Opis fizyczny

Bibliogr. 55 poz., rys., tab., wykr.

Twórcy

autor

Zhang T.

zhangtao14@mails.jlu.edu.cn

College of Communication Engineering, Jilin University, Changchun, China

autor

Chen W.

chenwz@jlu.edu.cn

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

autor

Li M.

mingyang15@mails.jlu.edu.cn

College of Communication Engineering, Jilin University, Changchun, China

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-d8373fe6-a2f7-4e57-ac7a-88799c53490f