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Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness

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Brink-Kjaer, Andreas ; Olesen, Alexander Neergaard ; Peppard, Paul E ; Stone, Katie L ; Jennum, Poul ; Mignot, Emmanuel ; Sorensen, Helge B D. / Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness. In: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 2020 ; Vol. 131, No. 6. pp. 1187-1203.

Bibtex

@article{789e16b6cb8247929bf6b23f1f64c937,
title = "Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness",
abstract = "OBJECTIVE: Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals.METHODS: A deep neural network was trained on 2,889 PSGs to detect cortical arousals and wakefulness in 1-second intervals. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects.RESULTS: In a dataset of 1,026 PSGs, the MAD achieved an F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by nine expert technicians, the MAD performed comparable to four and significantly outperformed five expert technicians for arousal detection. After controlling for known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds (p = 0.0075).CONCLUSIONS: The MAD performed better or comparable to human expert scorers. The MAD-predicted arousals were shown to be significant predictors of MSL.SIGNIFICANCE: This study validates a fully automatic method for scoring arousals in PSGs.",
keywords = "Arousal, Automatic detection, Daytime sleepiness, Deep neural networks, MSLT, Polysomnography",
author = "Andreas Brink-Kjaer and Olesen, {Alexander Neergaard} and Peppard, {Paul E} and Stone, {Katie L} and Poul Jennum and Emmanuel Mignot and Sorensen, {Helge B D}",
note = "Copyright {\textcopyright} 2020 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.",
year = "2020",
month = jun,
doi = "10.1016/j.clinph.2020.02.027",
language = "English",
volume = "131",
pages = "1187--1203",
journal = "Clinical Neurophysiology",
issn = "1388-2457",
publisher = "Elsevier Ireland Ltd",
number = "6",

}

RIS

TY - JOUR

T1 - Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness

AU - Brink-Kjaer, Andreas

AU - Olesen, Alexander Neergaard

AU - Peppard, Paul E

AU - Stone, Katie L

AU - Jennum, Poul

AU - Mignot, Emmanuel

AU - Sorensen, Helge B D

N1 - Copyright © 2020 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

PY - 2020/6

Y1 - 2020/6

N2 - OBJECTIVE: Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals.METHODS: A deep neural network was trained on 2,889 PSGs to detect cortical arousals and wakefulness in 1-second intervals. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects.RESULTS: In a dataset of 1,026 PSGs, the MAD achieved an F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by nine expert technicians, the MAD performed comparable to four and significantly outperformed five expert technicians for arousal detection. After controlling for known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds (p = 0.0075).CONCLUSIONS: The MAD performed better or comparable to human expert scorers. The MAD-predicted arousals were shown to be significant predictors of MSL.SIGNIFICANCE: This study validates a fully automatic method for scoring arousals in PSGs.

AB - OBJECTIVE: Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals.METHODS: A deep neural network was trained on 2,889 PSGs to detect cortical arousals and wakefulness in 1-second intervals. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects.RESULTS: In a dataset of 1,026 PSGs, the MAD achieved an F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by nine expert technicians, the MAD performed comparable to four and significantly outperformed five expert technicians for arousal detection. After controlling for known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds (p = 0.0075).CONCLUSIONS: The MAD performed better or comparable to human expert scorers. The MAD-predicted arousals were shown to be significant predictors of MSL.SIGNIFICANCE: This study validates a fully automatic method for scoring arousals in PSGs.

KW - Arousal

KW - Automatic detection

KW - Daytime sleepiness

KW - Deep neural networks

KW - MSLT

KW - Polysomnography

U2 - 10.1016/j.clinph.2020.02.027

DO - 10.1016/j.clinph.2020.02.027

M3 - Journal article

C2 - 32299002

VL - 131

SP - 1187

EP - 1203

JO - Clinical Neurophysiology

JF - Clinical Neurophysiology

SN - 1388-2457

IS - 6

ER -

ID: 61831161