NREM sleep staging using WAVCNS index

Agrawal, Gracee; Modarres, Mohammad; Zikov, T.; Bibian, S.

doi:10.1007/s10877-011-9290-4

Cited by 3 publications

(5 citation statements)

References 15 publications

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“…As indicated in the Introduction a number of previous studies evaluated the agreement between automatic scoring using frontal signals and manual scoring by one to three scorers. [9][10][11][12][13][14][15][16][17] Although some of the reported results are promising, these approaches are far from being available for widespread use in clinical home testing for a number of reasons:…”

Section: Scoring Sleep With Frontal Versus Central Derivationsmentioning

confidence: 99%

“…3. These studies utilized automatic scoring systems that are built into specific commercial products not certified for diagnosis of sleep disorders [9][10][11]13,14 or were applied using general analytical tools (e.g., MATLAB 15,17 or unspecified 12,16 ). Assuming the good results are confirmed in larger studies, much development work would need to be done to adapt such automatic systems for use by standard EEG recording equipment.…”

Section: Scoring Sleep With Frontal Versus Central Derivationsmentioning

confidence: 99%

“…A number of investigations reported on the agreement between automatic scoring using facial electrodes (electro-ocular or frontal) and manual scoring. [9][10][11][12][13][14][15][16][17] With few exceptions 11,15,16 these studies involved a small number of normal subjects and in all but one 16 validation was against the scoring of one or two local technologists. Although agreement with manual scoring was acceptable in normal subjects, it deteriorated when significant sleep-disordered breathing was present.…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of Automatic Polysomnography Scoring Using Frontal Electrodes

Younes

Giannouli

2016

Journal of Clinical Sleep Medicine

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Section: Scoring Sleep With Frontal Versus Central Derivationsmentioning

confidence: 99%

Section: Scoring Sleep With Frontal Versus Central Derivationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accuracy of Automatic Polysomnography Scoring Using Frontal Electrodes

Younes

Giannouli

2016

Journal of Clinical Sleep Medicine

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“…2008; Sinha 2008; Fraiwan et al . 2010, 2011; Garces Correa & Laciar Leber 2010; Agrawal et al . 2011), bispectral density (Acharya et al .…”

Section: Introductionmentioning

confidence: 99%

“…Other characters have also been proposed, including information that is harder for humans to intuitively interpret. These include coefficients of wavelet analysis (Ebrahimi et al 2008;Gabran et al 2008;Sinha 2008;Fraiwan et al 2010Fraiwan et al , 2011Garces Correa & Laciar Leber 2010;Agrawal et al 2011), bispectral density (Acharya et al 2010;Swarnkar et al 2010), parameters of multichannel autoregressive modeling (Zhovna & Shallom 2008) and matching pursuit method with slow wave patterns (Picot et al 2011). Recently, two groups have proposed using a broader range of the EEG spectrum, more finely binned than the three classical bands, and reported higher-quality sleep stage classification (Vivaldi & Bassi 2006;Rytk€ onen et al 2011).…”

Section: Introductionmentioning

confidence: 99%

FASTER: an unsupervised fully automated sleep staging method for mice

Sunagawa¹,

Séi

Shimba

et al. 2013

Genes to Cells

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Identifying the stages of sleep, or sleep staging, is an unavoidable step in sleep research and typically requires visual inspection of electroencephalography (EEG) and electromyography (EMG) data. Currently, scoring is slow, biased and prone to error by humans and thus is the most important bottleneck for large-scale sleep research in animals. We have developed an unsupervised, fully automated sleep staging method for mice that allows less subjective and high-throughput evaluation of sleep. Fully Automated Sleep sTaging method via EEG/EMG Recordings (FASTER) is based on nonparametric density estimation clustering of comprehensive EEG/EMG power spectra. FASTER can accurately identify sleep patterns in mice that have been perturbed by drugs or by genetic modification of a clock gene. The overall accuracy is over 90% in every group. 24-h data are staged by a laptop computer in 10 min, which is faster than an experienced human rater. Dramatically improving the sleep staging process in both quality and throughput FASTER will open the door to quantitative and comprehensive animal sleep research.

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Feasibility of continuous sedation monitoring in critically ill intensive care unit patients using the NeuroSENSE WAVCNS index

West

McBeth

Brodie

et al. 2018

J Clin Monit Comput

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Sedation in the intensive care unit (ICU) is challenging, as both over- and under-sedation are detrimental. Current methods of assessment, such as the Richmond Agitation Sedation Scale (RASS), are measured intermittently and rely on patients' behavioral response to stimulation, which may interrupt sleep/rest. A non-stimulating method for continuous sedation monitoring may be beneficial and allow more frequent assessment. Processed electroencephalography (EEG) monitors have not been routinely adopted in the ICU. The aim of this observational study was to assess the feasibility of using the NeuroSENSE™ monitor for EEG-based continuous sedation assessment. With ethical approval, ICU patients on continuous propofol sedation were recruited. Depth-of-hypnosis index (WAV) values were obtained from the NeuroSENSE. Bedside nurses, blinded to the NeuroSENSE, performed regular RASS assessments and maintained the sedation regimen as per standard of care. Participants were monitored throughout the duration of their propofol infusion, up to 24 h. Fifteen patients, with median [interquartile range] age of 57 [52-62.5] years were each monitored for a duration of 9.0 [5.7-20.1] h. Valid WAV values were obtained for 89% [66-99] of monitoring time and were widely distributed within and between individuals, with 6% [1-31] spent < 40 (very deep), and 3% [1-15] spent > 90 (awake). Significant EEG suppression was detected in 3/15 (20%) participants. Observed RASS matched RASS goals in 36/89 (40%) assessments. The WAV variability, and incidence of EEG suppression, highlight the limitations of using RASS as a standalone sedation measure, and suggests potential benefit of adjunct continuous brain monitoring.

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NREM sleep staging using WAVCNS index

Cited by 3 publications

References 15 publications

Accuracy of Automatic Polysomnography Scoring Using Frontal Electrodes

Accuracy of Automatic Polysomnography Scoring Using Frontal Electrodes

FASTER: an unsupervised fully automated sleep staging method for mice

Feasibility of continuous sedation monitoring in critically ill intensive care unit patients using the NeuroSENSE WAVCNS index

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