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 language | English |
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Title of host publication | Proceedings of the 2024 Australasian Computer Science Week, ACSW 2024 |
Publisher | Association for Computing Machinery |
Pages | 43-46 |
Number of pages | 4 |
ISBN (Electronic) | 9798400717307 |
DOIs | |
Publication status | Published - 29 Jan 2024 |
Event | 2024 Australasian Computer Science Week, ACSW 2024 - Sydney, Australia Duration: 29 Jan 2024 → 01 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
Name | ACM International Conference Proceeding Series |
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Conference
Conference | 2024 Australasian Computer Science Week, ACSW 2024 |
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Country/Territory | Australia |
City | Sydney |
Period | 29/01/24 → 01/02/24 |
Internet address |
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