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  5. Epileptic Seizure Detection in Clinical EEGs Using an XGboost-based Method
 
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Epileptic Seizure Detection in Clinical EEGs Using an XGboost-based Method

Author(s)
Wei, Lan  
Mooney, Catherine  
Uri
http://hdl.handle.net/10197/13124
Date Issued
2020-12-05
Date Available
2022-09-20T15:34:45Z
Abstract
Epilepsy is one of the most common serious disorders of the brain, affecting about 50 million people worldwide. Electroencephalography (EEG) is an electrophysiological monitoring method which is used to measure tiny electrical changes of the brain, and it is frequently used to diagnose epilepsy. However, the visual annotation of EEG traces is time-consuming and typically requires experienced experts. Therefore, automatic seizure detection can help to reduce the time required to annotate EEGs. Automatic detection of seizures in clinical EEGs has been limited-to date. In this study, we present an XGBoost-based method to detect seizures in EEGs from the TUH-EEG Corpus. 4,597 EEG files were used to train the method, 1,013 EEGs were used as a validation set, and 1,026 EEG files were used to test the method. Sixty-four features were selected as the input to the training set, and Synthetic Minority Over-sampling Technique was used to balance the dataset. Our XGBoost-based method achieved sensitivity and false alarm/24 hours of 20.00% and 15.59, respectively, in the test set. The proposed XGBoost-based method has the potential to help researchers automatically analyse seizures in clinical EEG recordings.
Sponsorship
European Commission - European Regional Development Fund
Science Foundation Ireland
Other Sponsorship
FutureNeuro industry partners
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2020 IEEE
Subjects

TUH-EEG

Corpus

Seizure detection

Epilepsy

Machine learning

DOI
10.1109/SPMB50085.2020.9353625
Web versions
https://www.ieeespmb.org/2020/
Language
English
Status of Item
Peer reviewed
Journal
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/
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Neureka_Epilepsy_Challenge_Lan.pdf

Size

830.72 KB

Format

Adobe PDF

Checksum (MD5)

5d75baf08e92a18ad46e047fcbbc181c

Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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