EEG sleep stages identification based on weighted undirected complex networks
Article
Article Title | EEG sleep stages identification based on weighted undirected complex networks |
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ERA Journal ID | 5039 |
Article Category | Article |
Authors | Diykh, Mohammed (Author), Li, Yan (Author) and Abdulla, Shahab (Author) |
Journal Title | Computer Methods and Programs in Biomedicine |
Journal Citation | 184, pp. 1-14 |
Article Number | 105116 |
Number of Pages | 14 |
Year | 2020 |
Publisher | Elsevier |
Place of Publication | Ireland |
ISSN | 0169-2607 |
1872-7565 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cmpb.2019.105116 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0169260718313257 |
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 Results Conclusions |
Keywords | Sleep stages; Weighted networks; Statistical model; EEG single channel |
ANZSRC Field of Research 2020 | 429999. Other health sciences not elsewhere classified |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
Open Access College | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5683/eeg-sleep-stages-identification-based-on-weighted-undirected-complex-networks
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