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Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings

Arousals during sleep are transient accelerations of the EEG signal, considered to reflect sleep perturbations associated with poorer sleep quality. They are typically detected by visual inspection, which is time consuming, subjective, and prevents good comparability across scorers, studies and rese...

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Autores principales: Chylinski, Daphne, Rudzik, Franziska, Coppieters ‘t Wallant, Dorothée, Grignard, Martin, Vandeleene, Nora, Van Egroo, Maxime, Thiesse, Laurie, Solbach, Stig, Maquet, Pierre, Phillips, Christophe, Vandewalle, Gilles, Cajochen, Christian, Muto, Vincenzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115937/
https://www.ncbi.nlm.nih.gov/pubmed/32803153
http://dx.doi.org/10.3390/clockssleep2030020
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author Chylinski, Daphne
Rudzik, Franziska
Coppieters ‘t Wallant, Dorothée
Grignard, Martin
Vandeleene, Nora
Van Egroo, Maxime
Thiesse, Laurie
Solbach, Stig
Maquet, Pierre
Phillips, Christophe
Vandewalle, Gilles
Cajochen, Christian
Muto, Vincenzo
author_facet Chylinski, Daphne
Rudzik, Franziska
Coppieters ‘t Wallant, Dorothée
Grignard, Martin
Vandeleene, Nora
Van Egroo, Maxime
Thiesse, Laurie
Solbach, Stig
Maquet, Pierre
Phillips, Christophe
Vandewalle, Gilles
Cajochen, Christian
Muto, Vincenzo
author_sort Chylinski, Daphne
collection PubMed
description Arousals during sleep are transient accelerations of the EEG signal, considered to reflect sleep perturbations associated with poorer sleep quality. They are typically detected by visual inspection, which is time consuming, subjective, and prevents good comparability across scorers, studies and research centres. We developed a fully automatic algorithm which aims at detecting artefact and arousal events in whole-night EEG recordings, based on time-frequency analysis with adapted thresholds derived from individual data. We ran an automated detection of arousals over 35 sleep EEG recordings in healthy young and older individuals and compared it against human visual detection from two research centres with the aim to evaluate the algorithm performance. Comparison across human scorers revealed a high variability in the number of detected arousals, which was always lower than the number detected automatically. Despite indexing more events, automatic detection showed high agreement with human detection as reflected by its correlation with human raters and very good Cohen’s kappa values. Finally, the sex of participants and sleep stage did not influence performance, while age may impact automatic detection, depending on the human rater considered as gold standard. We propose our freely available algorithm as a reliable and time-sparing alternative to visual detection of arousals.
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spelling pubmed-71159372020-09-01 Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings Chylinski, Daphne Rudzik, Franziska Coppieters ‘t Wallant, Dorothée Grignard, Martin Vandeleene, Nora Van Egroo, Maxime Thiesse, Laurie Solbach, Stig Maquet, Pierre Phillips, Christophe Vandewalle, Gilles Cajochen, Christian Muto, Vincenzo Clocks Sleep Article Arousals during sleep are transient accelerations of the EEG signal, considered to reflect sleep perturbations associated with poorer sleep quality. They are typically detected by visual inspection, which is time consuming, subjective, and prevents good comparability across scorers, studies and research centres. We developed a fully automatic algorithm which aims at detecting artefact and arousal events in whole-night EEG recordings, based on time-frequency analysis with adapted thresholds derived from individual data. We ran an automated detection of arousals over 35 sleep EEG recordings in healthy young and older individuals and compared it against human visual detection from two research centres with the aim to evaluate the algorithm performance. Comparison across human scorers revealed a high variability in the number of detected arousals, which was always lower than the number detected automatically. Despite indexing more events, automatic detection showed high agreement with human detection as reflected by its correlation with human raters and very good Cohen’s kappa values. Finally, the sex of participants and sleep stage did not influence performance, while age may impact automatic detection, depending on the human rater considered as gold standard. We propose our freely available algorithm as a reliable and time-sparing alternative to visual detection of arousals. MDPI 2020-07-16 /pmc/articles/PMC7115937/ /pubmed/32803153 http://dx.doi.org/10.3390/clockssleep2030020 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chylinski, Daphne
Rudzik, Franziska
Coppieters ‘t Wallant, Dorothée
Grignard, Martin
Vandeleene, Nora
Van Egroo, Maxime
Thiesse, Laurie
Solbach, Stig
Maquet, Pierre
Phillips, Christophe
Vandewalle, Gilles
Cajochen, Christian
Muto, Vincenzo
Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings
title Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings
title_full Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings
title_fullStr Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings
title_full_unstemmed Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings
title_short Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings
title_sort validation of an automatic arousal detection algorithm for whole-night sleep eeg recordings
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7115937/
https://www.ncbi.nlm.nih.gov/pubmed/32803153
http://dx.doi.org/10.3390/clockssleep2030020
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