Cargando…
Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke
In this study, we demonstrated the use of low-cost portable electroencephalography (EEG) as a method for prehospital stroke diagnosis. We used a portable EEG system to record data from 25 participants, 16 had acute ischemic stroke events, and compared the results to age-matched controls that include...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7595199/ https://www.ncbi.nlm.nih.gov/pubmed/33116187 http://dx.doi.org/10.1038/s41598-020-75379-w |
_version_ | 1783601816800329728 |
---|---|
author | Wilkinson, Cassandra M. Burrell, Jennifer I. Kuziek, Jonathan W. P. Thirunavukkarasu, Sibi Buck, Brian H. Mathewson, Kyle E. |
author_facet | Wilkinson, Cassandra M. Burrell, Jennifer I. Kuziek, Jonathan W. P. Thirunavukkarasu, Sibi Buck, Brian H. Mathewson, Kyle E. |
author_sort | Wilkinson, Cassandra M. |
collection | PubMed |
description | In this study, we demonstrated the use of low-cost portable electroencephalography (EEG) as a method for prehospital stroke diagnosis. We used a portable EEG system to record data from 25 participants, 16 had acute ischemic stroke events, and compared the results to age-matched controls that included stroke mimics. Delta/alpha ratio (DAR), (delta + theta)/(alpha + beta) ratio (DBATR) and pairwise-derived Brain Symmetry Index (pdBSI) were investigated, as well as head movement using the on-board accelerometer and gyroscope. We then used machine learning to distinguish between different subgroups. DAR and DBATR increased in ischemic stroke patients with increasing stroke severity (p = 0.0021, partial η(2) = 0.293; p = 0.01, partial η(2) = 0.234). Also, pdBSI decreased in low frequencies and increased in high frequencies in patients who had a stroke (p = 0.036, partial η(2) = 0.177). Using classification trees, we were able to distinguish moderate to severe stroke patients and from minor stroke and controls, with a 63% sensitivity, 86% specificity and accuracy of 76%. There are significant differences in DAR, DBATR, and pdBSI between patients with ischemic stroke when compared to controls, and these effects scale with severity. We have shown the utility of a low-cost portable EEG system to aid in patient triage and diagnosis as an early detection tool. |
format | Online Article Text |
id | pubmed-7595199 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75951992020-10-29 Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke Wilkinson, Cassandra M. Burrell, Jennifer I. Kuziek, Jonathan W. P. Thirunavukkarasu, Sibi Buck, Brian H. Mathewson, Kyle E. Sci Rep Article In this study, we demonstrated the use of low-cost portable electroencephalography (EEG) as a method for prehospital stroke diagnosis. We used a portable EEG system to record data from 25 participants, 16 had acute ischemic stroke events, and compared the results to age-matched controls that included stroke mimics. Delta/alpha ratio (DAR), (delta + theta)/(alpha + beta) ratio (DBATR) and pairwise-derived Brain Symmetry Index (pdBSI) were investigated, as well as head movement using the on-board accelerometer and gyroscope. We then used machine learning to distinguish between different subgroups. DAR and DBATR increased in ischemic stroke patients with increasing stroke severity (p = 0.0021, partial η(2) = 0.293; p = 0.01, partial η(2) = 0.234). Also, pdBSI decreased in low frequencies and increased in high frequencies in patients who had a stroke (p = 0.036, partial η(2) = 0.177). Using classification trees, we were able to distinguish moderate to severe stroke patients and from minor stroke and controls, with a 63% sensitivity, 86% specificity and accuracy of 76%. There are significant differences in DAR, DBATR, and pdBSI between patients with ischemic stroke when compared to controls, and these effects scale with severity. We have shown the utility of a low-cost portable EEG system to aid in patient triage and diagnosis as an early detection tool. Nature Publishing Group UK 2020-10-28 /pmc/articles/PMC7595199/ /pubmed/33116187 http://dx.doi.org/10.1038/s41598-020-75379-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Wilkinson, Cassandra M. Burrell, Jennifer I. Kuziek, Jonathan W. P. Thirunavukkarasu, Sibi Buck, Brian H. Mathewson, Kyle E. Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke |
title | Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke |
title_full | Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke |
title_fullStr | Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke |
title_full_unstemmed | Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke |
title_short | Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke |
title_sort | predicting stroke severity with a 3-min recording from the muse portable eeg system for rapid diagnosis of stroke |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7595199/ https://www.ncbi.nlm.nih.gov/pubmed/33116187 http://dx.doi.org/10.1038/s41598-020-75379-w |
work_keys_str_mv | AT wilkinsoncassandram predictingstrokeseveritywitha3minrecordingfromthemuseportableeegsystemforrapiddiagnosisofstroke AT burrelljenniferi predictingstrokeseveritywitha3minrecordingfromthemuseportableeegsystemforrapiddiagnosisofstroke AT kuziekjonathanwp predictingstrokeseveritywitha3minrecordingfromthemuseportableeegsystemforrapiddiagnosisofstroke AT thirunavukkarasusibi predictingstrokeseveritywitha3minrecordingfromthemuseportableeegsystemforrapiddiagnosisofstroke AT buckbrianh predictingstrokeseveritywitha3minrecordingfromthemuseportableeegsystemforrapiddiagnosisofstroke AT mathewsonkylee predictingstrokeseveritywitha3minrecordingfromthemuseportableeegsystemforrapiddiagnosisofstroke |