Detailed Assessment of Sleep Architecture with Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients with Obstructive Sleep Apnea

Traditional sleep staging with non-overlapping 30-second epochs overlooks multiple sleep-wake transitions. We aimed to overcome this by analyzing the sleep architecture in more detail with deep learning methods and hypothesized that the traditional sleep staging underestimates the sleep fragmentatio...

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Bibliographic Details
Main Authors:	Aakko, J. (Author), Afara, I.O (Author), Duce, B. (Author), Kainulainen, S. (Author), Kalevo, L. (Author), Korkalainen, H. (Author), Leino, A. (Author), Leppanen, T. (Author), Myllymaa, S. (Author), Toyras, J. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	adult Architecture Article cancer staging deep learning Deep learning Deep Learning disease severity electroencephalography female fragmented sleep human Humans interrater reliability Learning methods Learning systems machine learning major clinical study male middle aged obstructive sleep apnea Obstructive sleep apnea polysomnography Polysomnography REM sleep sleep Sleep Sleep Apnea, Obstructive Sleep architecture sleep deprivation Sleep Deprivation sleep disorder sleep disordered breathing Sleep disorders Sleep efficiencies sleep efficiency sleep fragmentation sleep quality Sleep research sleep stage Sleep Stages sleep staging sleep time snoring support vector machine time series analysis Traditional approaches Wake transition wakefulness Wakes
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Detailed Assessment of Sleep Architecture with Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients with Obstructive Sleep Apnea

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