EEG seizure detection via wavelet variance

Paul Grant

Research output: Book chapter/Published conference paperConference paperpeer-review

1 Citation (Scopus)
20 Downloads (Pure)

Abstract

Epilepsy is one of the most prevalent neurological conditions, where an epileptic seizure is a transient occurrence due to abnormal, excessive and synchronous activity in the brain. Electroencephalogram signals emanating from the brain may be captured, analysed and then play a significant role in detection and prediction of epileptic seizures. The intention here is to leverage off the abilities of the the Maximum Overlap Discrete Wavelet Transform to provide analysis of variance exhibited at differing inherent frequency levels and to develop develop various graph based metrics of connection between the electrodes placed upon the scalp. Using statistical parameters derived from these graph theoretic indicators for electrode connectivity, build the attribute space. The entire exercise is to be undertaken in open-source software and publicly available data.

Original languageEnglish
Title of host publicationProceedings of the 2024 Australasian Computer Science Week, ACSW 2024
PublisherAssociation for Computing Machinery
Pages43-46
Number of pages4
ISBN (Electronic)9798400717307
DOIs
Publication statusPublished - 29 Jan 2024
Event2024 Australasian Computer Science Week, ACSW 2024 - Sydney, Australia
Duration: 29 Jan 202401 Feb 2024
https://acsw.core.edu.au/2024-home (Conference website)
https://dl.acm.org/action/showFmPdf?doi=10.1145%2F3641142 (Front matter)

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2024 Australasian Computer Science Week, ACSW 2024
Country/TerritoryAustralia
CitySydney
Period29/01/2401/02/24
Internet address

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