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

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Diykh, Mohammed ORCID: 0000-0003-0018-4199 (external link), Miften, FS ORCID: 0000-0002-3557-2194 (external link), Abdullaf, Shahab, Deo, Ravinesh C, Siuly, Siuly ORCID: 0000-0003-2491-0546 (external link), 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

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/45465
DOI 10.1016/j.measurement.2022.110731 (external link)
Official URL https://www.sciencedirect.com/science/article/pii/... (external link)
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 (external link)
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