Efficacy of the Sequential Administration of Melatonin, Hydroxyzine, and Chloral Hydrate for Recording Sleep EEGs in Children

Sedation of children for electroencephalography (EEG) recordings is often required. Chloral hydrate (CH) requires medical clearance and continuous monitoring. To try to reduce personnel and time resources associated with CH administration, a new sedation policy was formulated. This study included all children who underwent an EEG during a consecutive 3-month period following the implementation of the new sedation policy, which consists of the sequential administration of melatonin, hydroxyzine (if needed), and CH (if needed). The comparator group included all children with a recorded EEG during a consecutive 3-month period when the sedation policy consisted of the sole administration of CH. A total of 803 children with a mean age of 7.9 years (SD = 5.1, range = 0.5-17.7 years) were included. Sleep EEG recordings were obtained in 364 of 385 children (94.6%) using the old sedation policy and in 409 of 418 children (97.9%) using the new one. With the new sedation policy, the percentage of children requiring CH dropped from 37.1% to 6.7% ( P < .001). Time to sleep onset and duration of sleep were not significantly different between the 2 policies. The new sedation policy was very well tolerated. The new sedation policy is very safe, is highly efficacious in obtaining sleep EEG recordings, and will result in substantial saving of time and personnel resources.

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Melatonin versus chloral hydrate for recording sleep EEG

European Journal of Paediatric Neurology ◽

10.1016/j.ejpn.2009.06.010 ◽

2010 ◽

Vol 14 (3) ◽

pp. 235-238 ◽

Cited By ~ 26

Author(s):

Mahmoud Reza Ashrafi ◽

Mahmoud Mohammadi ◽

Javad Tafarroji ◽

Reza Shabanian ◽

Peyman Salamati ◽

...

Keyword(s):

Chloral Hydrate ◽

Sleep Eeg

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Changes in EEG permutation entropy in the evening and in the transition from wake to sleep

SLEEP ◽

10.1093/sleep/zsaa226 ◽

2020 ◽

Author(s):

Fengzhen Hou ◽

Lulu Zhang ◽

Baokun Qin ◽

Giulia Gaggioni ◽

Xinyu Liu ◽

...

Keyword(s):

Sleep Onset ◽

Sleep Time ◽

Large Datasets ◽

Permutation Entropy ◽

Brain State ◽

Eeg Signal ◽

Spectral Measures ◽

Progressive Change ◽

Eeg Recordings ◽

The Brain

Abstract Quantifying the complexity of the EEG signal during prolonged wakefulness and during sleep is gaining interest as an additional mean to characterize the mechanisms associated with sleep and wakefulness regulation. Here, we characterized how EEG complexity, as indexed by Multiscale Permutation Entropy (MSPE), changed progressively in the evening prior to light off and during the transition from wakefulness to sleep. We further explored whether MSPE was able to discriminate between wakefulness and sleep around sleep onset and whether MSPE changes were correlated with spectral measures of the EEG related to sleep need during concomitant wakefulness (theta power—Ptheta: 4–8 Hz). To address these questions, we took advantage of large datasets of several hundred of ambulatory EEG recordings of individual of both sexes aged 25–101 years. Results show that MSPE significantly decreases before light off (i.e. before sleep time) and in the transition from wakefulness to sleep onset. Furthermore, MSPE allows for an excellent discrimination between pre-sleep wakefulness and early sleep. Finally, we show that MSPE is correlated with concomitant Ptheta. Yet, the direction of the latter correlation changed from before light-off to the transition to sleep. Given the association between EEG complexity and consciousness, MSPE may track efficiently putative changes in consciousness preceding sleep onset. An MSPE stands as a comprehensive measure that is not limited to a given frequency band and reflects a progressive change brain state associated with sleep and wakefulness regulation. It may be an effective mean to detect when the brain is in a state close to sleep onset.

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Diagnostic yield of standard-wake and sleep EEG recordings

Clinical Neurophysiology ◽

10.1016/j.clinph.2018.01.056 ◽

2018 ◽

Vol 129 (4) ◽

pp. 713-716 ◽

Cited By ~ 3

Author(s):

Pirgit Meritam ◽

Elena Gardella ◽

Jørgen Alving ◽

Daniella Terney ◽

Melita Cacic Hribljan ◽

...

Keyword(s):

Diagnostic Yield ◽

Sleep Eeg ◽

Eeg Recordings

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Sleep EEG spectral power is correlated with subjective-objective discrepancy of sleep onset latency in major depressive disorder

Progress in Neuro-Psychopharmacology and Biological Psychiatry ◽

10.1016/j.pnpbp.2018.04.010 ◽

2018 ◽

Vol 85 ◽

pp. 122-127 ◽

Cited By ~ 5

Author(s):

Seung-Gul Kang ◽

Sara Mariani ◽

Stephanie A. Marvin ◽

Kwang-Pil Ko ◽

Susan Redline ◽

...

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Spectral Power ◽

Sleep Onset ◽

Sleep Eeg ◽

Sleep Onset Latency ◽

Onset Latency ◽

Major Depressive ◽

Eeg Spectral Power

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Sleep EEG characteristics associated with sleep onset misperception

Sleep Medicine ◽

10.1016/j.sleep.2019.01.031 ◽

2019 ◽

Vol 57 ◽

pp. 70-79 ◽

Cited By ~ 7

Author(s):

Lieke W.A. Hermans ◽

Tim R. Leufkens ◽

Merel M. van Gilst ◽

Tim Weysen ◽

Marco Ross ◽

...

Keyword(s):

Sleep Onset ◽

Sleep Eeg

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Comparison Between the Results of an Automatic and a Visual Scoring of Sleep EEG Recordings

SLEEP ◽

10.1093/sleep/12.4.354 ◽

1989 ◽

Keyword(s):

Sleep Eeg ◽

Visual Scoring ◽

Eeg Recordings

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Topographic distribution of temporal self-similarity properties of human sleep EEG recordings

Frontiers in Systems Neuroscience ◽

10.3389/conf.neuro.01.2009.04.228 ◽

2009 ◽

Vol 3 ◽

Author(s):

Roska Tamas

Keyword(s):

Sleep Eeg ◽

Self Similarity ◽

Human Sleep ◽

Eeg Recordings ◽

Topographic Distribution

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Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings

Clocks & Sleep ◽

10.3390/clockssleep2030020 ◽

2020 ◽

Vol 2 (3) ◽

pp. 258-272

Author(s):

Daphne Chylinski ◽

Franziska Rudzik ◽

Dorothée Coppieters ‘t Wallant ◽

Martin Grignard ◽

Nora Vandeleene ◽

...

Keyword(s):

Visual Detection ◽

Sleep Stage ◽

Automatic Detection ◽

Detection Algorithm ◽

Human Detection ◽

Sleep Eeg ◽

Older Individuals ◽

Time Frequency ◽

Eeg Recordings ◽

Research Centres

Arousals during sleep are transient accelerations of the EEG signal, considered to reflect sleep perturbations associated with poorer sleep quality. They are typically detected by visual inspection, which is time consuming, subjective, and prevents good comparability across scorers, studies and research centres. We developed a fully automatic algorithm which aims at detecting artefact and arousal events in whole-night EEG recordings, based on time-frequency analysis with adapted thresholds derived from individual data. We ran an automated detection of arousals over 35 sleep EEG recordings in healthy young and older individuals and compared it against human visual detection from two research centres with the aim to evaluate the algorithm performance. Comparison across human scorers revealed a high variability in the number of detected arousals, which was always lower than the number detected automatically. Despite indexing more events, automatic detection showed high agreement with human detection as reflected by its correlation with human raters and very good Cohen’s kappa values. Finally, the sex of participants and sleep stage did not influence performance, while age may impact automatic detection, depending on the human rater considered as gold standard. We propose our freely available algorithm as a reliable and time-sparing alternative to visual detection of arousals.

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Dexmedetomidine-induced deep sedation mimics non-rapid eye movement stage 3 sleep: large-scale validation using machine learning

SLEEP ◽

10.1093/sleep/zsaa167 ◽

2020 ◽

Author(s):

Sowmya M Ramaswamy ◽

Maud A S Weerink ◽

Michel M R F Struys ◽

Sunil B Nagaraj

Keyword(s):

Machine Learning ◽

Random Forest ◽

Eye Movement ◽

Large Scale ◽

Deep Sedation ◽

Sleep Eeg ◽

Rapid Eye Movement ◽

Sleep Patterns ◽

Natural Sleep ◽

Eeg Recordings

Abstract Study Objectives Dexmedetomidine-induced electroencephalogram (EEG) patterns during deep sedation are comparable with natural sleep patterns. Using large-scale EEG recordings and machine learning techniques, we investigated whether dexmedetomidine-induced deep sedation indeed mimics natural sleep patterns. Methods We used EEG recordings from three sources in this study: 8,707 overnight sleep EEG and 30 dexmedetomidine clinical trial EEG. Dexmedetomidine-induced sedation levels were assessed using the Modified Observer’s Assessment of Alertness/Sedation (MOAA/S) score. We extracted 22 spectral features from each EEG recording using a multitaper spectral estimation method. Elastic-net regularization method was used for feature selection. We compared the performance of several machine learning algorithms (logistic regression, support vector machine, and random forest), trained on individual sleep stages, to predict different levels of the MOAA/S sedation state. Results The random forest algorithm trained on non-rapid eye movement stage 3 (N3) predicted dexmedetomidine-induced deep sedation (MOAA/S = 0) with area under the receiver operator characteristics curve >0.8 outperforming other machine learning models. Power in the delta band (0–4 Hz) was selected as an important feature for prediction in addition to power in theta (4–8 Hz) and beta (16–30 Hz) bands. Conclusions Using a large-scale EEG data-driven approach and machine learning framework, we show that dexmedetomidine-induced deep sedation state mimics N3 sleep EEG patterns. Clinical Trials Name—Pharmacodynamic Interaction of REMI and DMED (PIRAD), URL—https://clinicaltrials.gov/ct2/show/NCT03143972, and registration—NCT03143972.

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