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REM Sleep Stage Identification with Raw Single-Channel EEG
This paper focused on creating an interpretable model for automatic rapid eye movement (REM) and non-REM sleep stage scoring for a single-channel electroencephalogram (EEG). Many methods attempt to extract meaningful information to provide to a learning algorithm. This method attempts to let the mod...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525287/ https://www.ncbi.nlm.nih.gov/pubmed/37760176 http://dx.doi.org/10.3390/bioengineering10091074 |
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author | Toban, Gabriel Poudel, Khem Hong, Don |
author_facet | Toban, Gabriel Poudel, Khem Hong, Don |
author_sort | Toban, Gabriel |
collection | PubMed |
description | This paper focused on creating an interpretable model for automatic rapid eye movement (REM) and non-REM sleep stage scoring for a single-channel electroencephalogram (EEG). Many methods attempt to extract meaningful information to provide to a learning algorithm. This method attempts to let the model extract the meaningful interpretable information by providing a smaller number of time-invariant signal filters for five frequency ranges using five CNN algorithms. A bi-directional GRU algorithm was applied to the output to incorporate time transition information. Training and tests were run on the well-known sleep-EDF-expanded database. The best results produced 97% accuracy, 93% precision, and 89% recall. |
format | Online Article Text |
id | pubmed-10525287 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105252872023-09-28 REM Sleep Stage Identification with Raw Single-Channel EEG Toban, Gabriel Poudel, Khem Hong, Don Bioengineering (Basel) Article This paper focused on creating an interpretable model for automatic rapid eye movement (REM) and non-REM sleep stage scoring for a single-channel electroencephalogram (EEG). Many methods attempt to extract meaningful information to provide to a learning algorithm. This method attempts to let the model extract the meaningful interpretable information by providing a smaller number of time-invariant signal filters for five frequency ranges using five CNN algorithms. A bi-directional GRU algorithm was applied to the output to incorporate time transition information. Training and tests were run on the well-known sleep-EDF-expanded database. The best results produced 97% accuracy, 93% precision, and 89% recall. MDPI 2023-09-11 /pmc/articles/PMC10525287/ /pubmed/37760176 http://dx.doi.org/10.3390/bioengineering10091074 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Toban, Gabriel Poudel, Khem Hong, Don REM Sleep Stage Identification with Raw Single-Channel EEG |
title | REM Sleep Stage Identification with Raw Single-Channel EEG |
title_full | REM Sleep Stage Identification with Raw Single-Channel EEG |
title_fullStr | REM Sleep Stage Identification with Raw Single-Channel EEG |
title_full_unstemmed | REM Sleep Stage Identification with Raw Single-Channel EEG |
title_short | REM Sleep Stage Identification with Raw Single-Channel EEG |
title_sort | rem sleep stage identification with raw single-channel eeg |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525287/ https://www.ncbi.nlm.nih.gov/pubmed/37760176 http://dx.doi.org/10.3390/bioengineering10091074 |
work_keys_str_mv | AT tobangabriel remsleepstageidentificationwithrawsinglechanneleeg AT poudelkhem remsleepstageidentificationwithrawsinglechanneleeg AT hongdon remsleepstageidentificationwithrawsinglechanneleeg |