XGboost-based Method for Seizure Detection in Mouse Models of Epilepsy

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Title: XGboost-based Method for Seizure Detection in Mouse Models of Epilepsy
Authors: Wei, LanGerbatin, R.Mamad, O.Lowery, Madeleine M.Mooney, Catherine
Permanent link: http://hdl.handle.net/10197/13125
Date: 5-Dec-2020
Online since: 2022-09-20T15:44:50Z
Abstract: Epilepsy is a chronic neurological disease which affects over 50 million people worldwide [1], caused by the disruption of the finely tuned inhibitory and excitatory balance in brain networks, manifesting clinically as seizures. Electroencephalographic (EEG) monitoring in rodent disease models of epilepsy is critical in the understanding of disease mechanisms and the development of anti-seizure drugs. However, the visual annotation of EEG traces is time-consuming, and is complicated by different models and seizure types. Automated annotation systems can help to solve these problems by reducing expert annotation time and increasing the throughput and reliability of seizure quantification. As machine learning is becoming increasingly popular for modelling sequential signals such as EEG, several researchers have tried machine learning to detect seizures in EEG traces from mouse models of epilepsy. Most existing work [2], [3] can only detect seizures in single mouse models of epilepsy and research on multiple mouse models has been limited to-date.
Funding Details: European Commission - Seventh Framework Programme (FP7)
Science Foundation Ireland
Funding Details: FutureNeuro industry partners
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2020 IEEE
Keywords: Biological system modelingEpilepsyMachine learningBrain modelingMiceElectroencephalography
DOI: 10.1109/SPMB50085.2020.9353632
Other versions: https://www.ieeespmb.org/2020/
Language: en
Status of Item: Peer reviewed
Is part of: 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB 2020)
Conference Details: The 2020 IEEE Signal Processing in Medicine and Biology Symposium, Virtual Conference, 5 December 2020
ISBN: 9781728188201
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science Research Collection
Electrical and Electronic Engineering Research Collection

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