Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness
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Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness. / Brink-Kjaer, Andreas; Olesen, Alexander Neergaard; Peppard, Paul E.; Stone, Katie L.; Jennum, Poul; Mignot, Emmanuel; Sorensen, Helge B.D.
In: Clinical Neurophysiology, Vol. 131, No. 6, 2020, p. 1187-1203.Research output: Contribution to journal › Journal article › Research › peer-review
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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.
PY - 2020
Y1 - 2020
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
AN - SCOPUS:85083043632
VL - 131
SP - 1187
EP - 1203
JO - Clinical Neurophysiology
JF - Clinical Neurophysiology
SN - 1388-2457
IS - 6
ER -
ID: 260244843