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Automatic Computer-Based Detection of Epileptic Seizures
Automatic computer-based seizure detection and warning devices are important for objective seizure documentation, for SUDEP prevention, to avoid seizure related injuries and social embarrassments as a consequence of seizures, and to develop on demand epilepsy therapies. Automatic seizure detection s...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6095028/ https://www.ncbi.nlm.nih.gov/pubmed/30140254 http://dx.doi.org/10.3389/fneur.2018.00639 |
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author | Baumgartner, Christoph Koren, Johannes P. Rothmayer, Michaela |
author_facet | Baumgartner, Christoph Koren, Johannes P. Rothmayer, Michaela |
author_sort | Baumgartner, Christoph |
collection | PubMed |
description | Automatic computer-based seizure detection and warning devices are important for objective seizure documentation, for SUDEP prevention, to avoid seizure related injuries and social embarrassments as a consequence of seizures, and to develop on demand epilepsy therapies. Automatic seizure detection systems can be based on direct analysis of epileptiform discharges on scalp-EEG or intracranial EEG, on the detection of motor manifestations of epileptic seizures using surface electromyography (sEMG), accelerometry (ACM), video detection systems and mattress sensors and finally on the assessment of changes of physiologic parameters accompanying epileptic seizures measured by electrocardiography (ECG), respiratory monitors, pulse oximetry, surface temperature sensors, and electrodermal activity. Here we review automatic seizure detection based on scalp-EEG, ECG, and sEMG. Different seizure types affect preferentially different measurement parameters. While EEG changes accompany all types of seizures, sEMG and ACM are suitable mainly for detection of seizures with major motor manifestations. Therefore, seizure detection can be optimized by multimodal systems combining several measurement parameters. While most systems provide sensitivities over 70%, specificity expressed as false alarm rates still needs to be improved. Patients' acceptance and comfort of a specific device are of critical importance for its long-term application in a meaningful clinical way. |
format | Online Article Text |
id | pubmed-6095028 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-60950282018-08-23 Automatic Computer-Based Detection of Epileptic Seizures Baumgartner, Christoph Koren, Johannes P. Rothmayer, Michaela Front Neurol Neurology Automatic computer-based seizure detection and warning devices are important for objective seizure documentation, for SUDEP prevention, to avoid seizure related injuries and social embarrassments as a consequence of seizures, and to develop on demand epilepsy therapies. Automatic seizure detection systems can be based on direct analysis of epileptiform discharges on scalp-EEG or intracranial EEG, on the detection of motor manifestations of epileptic seizures using surface electromyography (sEMG), accelerometry (ACM), video detection systems and mattress sensors and finally on the assessment of changes of physiologic parameters accompanying epileptic seizures measured by electrocardiography (ECG), respiratory monitors, pulse oximetry, surface temperature sensors, and electrodermal activity. Here we review automatic seizure detection based on scalp-EEG, ECG, and sEMG. Different seizure types affect preferentially different measurement parameters. While EEG changes accompany all types of seizures, sEMG and ACM are suitable mainly for detection of seizures with major motor manifestations. Therefore, seizure detection can be optimized by multimodal systems combining several measurement parameters. While most systems provide sensitivities over 70%, specificity expressed as false alarm rates still needs to be improved. Patients' acceptance and comfort of a specific device are of critical importance for its long-term application in a meaningful clinical way. Frontiers Media S.A. 2018-08-09 /pmc/articles/PMC6095028/ /pubmed/30140254 http://dx.doi.org/10.3389/fneur.2018.00639 Text en Copyright © 2018 Baumgartner, Koren and Rothmayer. http://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 | Neurology Baumgartner, Christoph Koren, Johannes P. Rothmayer, Michaela Automatic Computer-Based Detection of Epileptic Seizures |
title | Automatic Computer-Based Detection of Epileptic Seizures |
title_full | Automatic Computer-Based Detection of Epileptic Seizures |
title_fullStr | Automatic Computer-Based Detection of Epileptic Seizures |
title_full_unstemmed | Automatic Computer-Based Detection of Epileptic Seizures |
title_short | Automatic Computer-Based Detection of Epileptic Seizures |
title_sort | automatic computer-based detection of epileptic seizures |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6095028/ https://www.ncbi.nlm.nih.gov/pubmed/30140254 http://dx.doi.org/10.3389/fneur.2018.00639 |
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