Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals

Download
Full text for this resource is not available from the Research Repository.
Export

Diykh, Mohammed ORCID: 0000-0003-0018-4199, Miften, FS ORCID: 0000-0002-3557-2194, Abdullaf, Shahab, Deo, Ravinesh C, Siuly, Siuly ORCID: 0000-0003-2491-0546, Green, Jonathan H and Oudahb, Atheer Y (2022) Texture analysis based graph approach for automatic detection of neonatal seizure from multi-channel EEG signals. Measurement, 190. ISSN 0263-2241

Dimensions Badge

Altmetric Badge

Item type Article
URI https://vuir.vu.edu.au/id/eprint/45465
DOI 10.1016/j.measurement.2022.110731
Official URL https://www.sciencedirect.com/science/article/pii/...
Subjects Current > FOR (2020) Classification > 4006 Communications engineering
Current > FOR (2020) Classification > 4602 Artificial intelligence
Current > FOR (2020) Classification > 4605 Data management and data science
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords seizure detection; electroencephalography; Morse Wavelet; local binary pattern algorithm; graph-based community detection algorithm; classification
Citations in Scopus 13 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login

CORE (COnnecting REpositories)