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A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection

Sudden unexpected death of epilepsy (SUDEP) is a catastrophic and fatal complication of epilepsy and is the primary cause of mortality in those who have uncontrolled seizures. While several multifactorial processes have been implicated including cardiac, respiratory, autonomic dysfunction leading to...

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Autores principales: Li, Xiaojin, Huang, Yan, Lhatoo, Samden D., Tao, Shiqiang, Vilella Bertran, Laura, Zhang, Guo-Qiang, Cui, Licong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9806125/
https://www.ncbi.nlm.nih.gov/pubmed/36601382
http://dx.doi.org/10.3389/fninf.2022.1040084
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author Li, Xiaojin
Huang, Yan
Lhatoo, Samden D.
Tao, Shiqiang
Vilella Bertran, Laura
Zhang, Guo-Qiang
Cui, Licong
author_facet Li, Xiaojin
Huang, Yan
Lhatoo, Samden D.
Tao, Shiqiang
Vilella Bertran, Laura
Zhang, Guo-Qiang
Cui, Licong
author_sort Li, Xiaojin
collection PubMed
description Sudden unexpected death of epilepsy (SUDEP) is a catastrophic and fatal complication of epilepsy and is the primary cause of mortality in those who have uncontrolled seizures. While several multifactorial processes have been implicated including cardiac, respiratory, autonomic dysfunction leading to arrhythmia, hypoxia, and cessation of cerebral and brainstem function, the mechanisms underlying SUDEP are not completely understood. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a potential risk marker for SUDEP, as studies have shown that prolonged PGES was significantly associated with a higher risk of SUDEP. Automated PGES detection techniques have been developed to efficiently obtain PGES durations for SUDEP risk assessment. However, real-world data recorded in epilepsy monitoring units (EMUs) may contain high-amplitude signals due to physiological artifacts, such as breathing, muscle, and movement artifacts, making it difficult to determine the end of PGES. In this paper, we present a hybrid approach that combines the benefits of unsupervised and supervised learning for PGES detection using multi-channel EEG recordings. A K-means clustering model is leveraged to group EEG recordings with similar artifact features. We introduce a new learning strategy for training a set of random forest (RF) models based on clustering results to improve PGES detection performance. Our approach achieved a 5-second tolerance-based detection accuracy of 64.92%, a 10-second tolerance-based detection accuracy of 79.85%, and an average predicted time distance of 8.26 seconds with 286 EEG recordings using leave-one-out (LOO) cross-validation. The results demonstrated that our hybrid approach provided better performance compared to other existing approaches.
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spelling pubmed-98061252023-01-03 A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection Li, Xiaojin Huang, Yan Lhatoo, Samden D. Tao, Shiqiang Vilella Bertran, Laura Zhang, Guo-Qiang Cui, Licong Front Neuroinform Neuroscience Sudden unexpected death of epilepsy (SUDEP) is a catastrophic and fatal complication of epilepsy and is the primary cause of mortality in those who have uncontrolled seizures. While several multifactorial processes have been implicated including cardiac, respiratory, autonomic dysfunction leading to arrhythmia, hypoxia, and cessation of cerebral and brainstem function, the mechanisms underlying SUDEP are not completely understood. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a potential risk marker for SUDEP, as studies have shown that prolonged PGES was significantly associated with a higher risk of SUDEP. Automated PGES detection techniques have been developed to efficiently obtain PGES durations for SUDEP risk assessment. However, real-world data recorded in epilepsy monitoring units (EMUs) may contain high-amplitude signals due to physiological artifacts, such as breathing, muscle, and movement artifacts, making it difficult to determine the end of PGES. In this paper, we present a hybrid approach that combines the benefits of unsupervised and supervised learning for PGES detection using multi-channel EEG recordings. A K-means clustering model is leveraged to group EEG recordings with similar artifact features. We introduce a new learning strategy for training a set of random forest (RF) models based on clustering results to improve PGES detection performance. Our approach achieved a 5-second tolerance-based detection accuracy of 64.92%, a 10-second tolerance-based detection accuracy of 79.85%, and an average predicted time distance of 8.26 seconds with 286 EEG recordings using leave-one-out (LOO) cross-validation. The results demonstrated that our hybrid approach provided better performance compared to other existing approaches. Frontiers Media S.A. 2022-12-19 /pmc/articles/PMC9806125/ /pubmed/36601382 http://dx.doi.org/10.3389/fninf.2022.1040084 Text en Copyright © 2022 Li, Huang, Lhatoo, Tao, Vilella Bertran, Zhang and Cui. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Li, Xiaojin
Huang, Yan
Lhatoo, Samden D.
Tao, Shiqiang
Vilella Bertran, Laura
Zhang, Guo-Qiang
Cui, Licong
A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection
title A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection
title_full A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection
title_fullStr A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection
title_full_unstemmed A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection
title_short A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection
title_sort hybrid unsupervised and supervised learning approach for postictal generalized eeg suppression detection
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9806125/
https://www.ncbi.nlm.nih.gov/pubmed/36601382
http://dx.doi.org/10.3389/fninf.2022.1040084
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