Andreas Brink-Kjaer1, Alexander Neergaard Olesen2, Paul E Peppard3, Katie L Stone4, Poul Jennum5, Emmanuel Mignot6, Helge B D Sorensen7. 1. Center for Sleep Sciences and Medicine, Stanford University, CA, USA; Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark; Danish Center for Sleep Medicine, Glostrup University Hospital, Glostrup, Denmark. Electronic address: andbri@dtu.dk. 2. Center for Sleep Sciences and Medicine, Stanford University, CA, USA; Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark; Danish Center for Sleep Medicine, Glostrup University Hospital, Glostrup, Denmark. 3. Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA. 4. Research Institute, California Pacific Medical Center, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. 5. Danish Center for Sleep Medicine, Glostrup University Hospital, Glostrup, Denmark. 6. Center for Sleep Sciences and Medicine, Stanford University, CA, USA. 7. Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
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.
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.
Authors: Poul Jennum; Helge B D Sorensen; Emmanuel Mignot; Andreas Brink-Kjaer; Eileen B Leary; Haoqi Sun; M Brandon Westover; Katie L Stone; Paul E Peppard; Nancy E Lane; Peggy M Cawthon; Susan Redline Journal: NPJ Digit Med Date: 2022-07-22
Authors: Jonathan Foldager; Paul E Peppard; Erika W Hagen; Katie L Stone; Daniel S Evans; Gregory J Tranah; Helge Sørensen; Poul Jennum; Emmanuel Mignot; Logan D Schneider Journal: J Clin Sleep Med Date: 2022-01-01 Impact factor: 4.062