EEG sleep stages identification based on weighted undirected complex networks

Diykh, Mohammed and Li, Yan and Abdulla, Shahab (2020) EEG sleep stages identification based on weighted undirected complex networks. Computer Methods and Programs in Biomedicine, 184 (Article 105116). pp. 1-14.

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Abstract

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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Faculty/School / Institute/Centre: Current - Open Access College (1 Jul 2013 -)
Date Deposited: 21 Jan 2020 00:38
Last Modified: 21 Jan 2020 00:38
Uncontrolled Keywords: Sleep stages; Weighted networks; Statistical model; EEG single channel
Fields of Research : 11 Medical and Health Sciences > 1199 Other Medical and Health Sciences > 119999 Medical and Health Sciences not elsewhere classified
Socio-Economic Objective: C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
Identification Number or DOI: 10.1016/j.cmpb.2019.105116
URI: http://eprints.usq.edu.au/id/eprint/37179

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