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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9806125 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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