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Tytuł pozycji:

EEG sleep stages identification based on weighted undirected complex networks.

Tytuł:
EEG sleep stages identification based on weighted undirected complex networks.
Autorzy:
Diykh M; School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia; College of Education for Pure Science, University of Thi-Qar, Iraq. Electronic address: .
Li Y; School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia. Electronic address: .
Abdulla S; Open Access College, University of Southern Queensland, Australia. Electronic address: .
Źródło:
Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2020 Feb; Vol. 184, pp. 105116. Date of Electronic Publication: 2019 Oct 09.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Publication: Limerick : Elsevier Scientific Publishers
Original Publication: Amsterdam : Elsevier Science Publishers, c1984-
MeSH Terms:
Sleep Stages*
Electroencephalography/*methods
Database Management Systems ; Humans ; Models, Statistical ; Support Vector Machine
Contributed Indexing:
Keywords: EEG single channel; Sleep stages; Statistical model; Weighted networks
Entry Date(s):
Date Created: 20191020 Date Completed: 20210106 Latest Revision: 20210106
Update Code:
20240105
DOI:
10.1016/j.cmpb.2019.105116
PMID:
31629158
Czasopismo naukowe
Background and Objective: Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks.
Methods: Each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks.
Results: In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals.
Conclusions: An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard.
Competing Interests: Declaration of Competing Interest Authors declare that there is no conflict of interest in this paper.
(Copyright © 2019. Published by Elsevier B.V.)

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