J Clin Monit Comput - NREM sleep staging using WAV(CNS) index.


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JECTIVE: Visual scoring of 30-s epochs of sleep data is not always adequate to show the dynamic structure of sleep in sufficient details. It is also prone to considerable inter- and intra-rater variability. Moreover, it involves considerable training and experience, and is very tedious, time-consuming, labor-intensive and costly. Hence, automatic sleep staging is needed to overcome these limitations. Since naturally occurring NREM sleep and anesthesia have been reported to possess various underlying neurophysiological similarities, EEG-based depth-of-anesthesia monitors have started to penetrate into sleep research. This study investigates the ability of WAV(CNS) index (as implemented in NeuroSENSE depth-of-anesthesia monitor) to detect NREM sleep stages and wake state for full overnight PSG data.METHODS: Full overnight PSG sleep data, obtained from 24 adolescents, was scored by a registered PSG technologist for different sleep stages. Retrospective analysis was performed on a single frontal channel using the WAV(CNS) algorithm. Non-parametric descriptive statistics were used to examine the relationship between WAV(CNS) index and sleep stages.RESULTS: A strong correlation (?=?0.9458) was found between the WAV(CNS) index and NREM sleep stages, with WAV(CNS) index values decreasing with increasing sleep stages. Moreover, there was no significant overlap between different NREM sleep stages as classified by the WAV(CNS) index, which was able to significantly differentiate (P<0.001) between all pairs of Awake and different NREM stages.CONCLUSIONS: This study demonstrates that changes in the depth of natural NREM sleep are reflected sensitively by changes in the WAV(CNS) index. Hence, WAV(CNS) index may serve as an automatic real-time indicator of depth of natural sleep with high temporal resolution, and can possibly be of great use for automated sleep staging in routine/postoperative somnographic studies.

Resumo Limpo

jectiv visual score s epoch sleep data alway adequ show dynam structur sleep suffici detail also prone consider inter intrarat variabl moreov involv consider train experi tedious timeconsum laborintens cost henc automat sleep stage need overcom limit sinc natur occur nrem sleep anesthesia report possess various under neurophysiolog similar eegbas depthofanesthesia monitor start penetr sleep research studi investig abil wavcn index implement neurosens depthofanesthesia monitor detect nrem sleep stage wake state full overnight psg datamethod full overnight psg sleep data obtain adolesc score regist psg technologist differ sleep stage retrospect analysi perform singl frontal channel use wavcn algorithm nonparametr descript statist use examin relationship wavcn index sleep stagesresult strong correl found wavcn index nrem sleep stage wavcn index valu decreas increas sleep stage moreov signific overlap differ nrem sleep stage classifi wavcn index abl signific differenti p pair awak differ nrem stagesconclus studi demonstr chang depth natur nrem sleep reflect sensit chang wavcn index henc wavcn index may serv automat realtim indic depth natur sleep high tempor resolut can possibl great use autom sleep stage routinepostop somnograph studi

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