OBJECTIVE: 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 (ρ = 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.
OBJECTIVE: 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 (ρ = 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.
Authors: Dagmar J Niedhart; Heiko A Kaiser; Eric Jacobsohn; Charles B Hantler; Alex S Evers; Michael S Avidan Journal: Anesthesiology Date: 2006-02 Impact factor: 7.892
Authors: Diederik Nieuwenhuijs; Emma L Coleman; Neil J Douglas; Gordon B Drummond; Albert Dahan Journal: Anesth Analg Date: 2002-01 Impact factor: 5.108
Authors: Laura E Nelson; Jun Lu; Tianzhi Guo; Clifford B Saper; Nicholas P Franks; Mervyn Maze Journal: Anesthesiology Date: 2003-02 Impact factor: 7.892
Authors: Nicholas West; Paul B McBeth; Sonia M Brodie; Klaske van Heusden; Sarah Sunderland; Guy A Dumont; Donald E G Griesdale; J Mark Ansermino; Matthias Görges Journal: J Clin Monit Comput Date: 2018-02-20 Impact factor: 2.502