Sleep EEG recordings in depressive disorders

Background. Recent studies have suggested that major depressive disorders are associated with a breakdown in the organization of ultradian rhythm in sleep EEG. The present study used cross-spectral analysis of sleep EEG to confirm this finding, in a larger-scale study, evaluating the influence of gender and age on ultradian rhythms in depression.Methods. Temporal coherence of ultradian (80–120 min) rhythms in beta, theta and delta, recorded from central and parietal sites, were compared in 120 symptomatic, unmedicated, depressed out-patients and 59 healthy normal controls.Results. Few macro-architectural differences were noted between patients and controls. However, interhemispheric beta and theta coherence and intrahemispheric coherence between beta and delta rhythms were significantly lower in depressed patients. Coherence measures were lowest in women with depression and highest in men in the control group, but were not strongly influenced by age. Over 65% of depressed patients were [ges ]2 standard deviations below normal on at least one coherence measure, in sharp contrast to less than 10% of patients on macro-architectural variables.Conclusions. It was concluded that dysregulation of ultradian rhythms characterizes the majority of depressed out-patients, primarily women, even when macro-architecture did not differentiate groups. The outcome of this study supports the view that the pathophysiology of depression is strongly influenced by gender. It was suggested that low temporal coherence in depression reflects a breakdown in the organization of sleep EEG rhythms within and between the two hemispheres.

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

Clinical EEG and Neuroscience ◽

10.1177/1550059415621830 ◽

2016 ◽

Vol 48 (1) ◽

pp. 41-47 ◽

Cited By ~ 5

Author(s):

Maya Dirani ◽

Wassim Nasreddine ◽

Jawad Melhem ◽

Maher Arabi ◽

Ahmad Beydoun

Keyword(s):

Continuous Monitoring ◽

Chloral Hydrate ◽

Sleep Onset ◽

Sleep Eeg ◽

Substantial Saving ◽

Comparator Group ◽

Medical Clearance ◽

Sequential Administration ◽

Eeg Recordings

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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Sleep and focal epilepsy-polygraphic sleep EEG recordings

International Journal of Psychophysiology ◽

10.1016/0167-8760(89)90088-3 ◽

1989 ◽

Vol 7 (2-4) ◽

pp. 141-142

Author(s):

J. Beránek ◽

M. Singerová

Keyword(s):

Focal Epilepsy ◽

Sleep Eeg ◽

Eeg Recordings

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Sleep-endocrine effects of mifepristone and megestrol acetate in healthy men

AJP Endocrinology and Metabolism ◽

10.1152/ajpendo.1998.274.1.e139 ◽

1998 ◽

Vol 274 (1) ◽

pp. E139-E145 ◽

Cited By ~ 3

Author(s):

Klaus Wiedemann ◽

Christoph J. Lauer ◽

Margarete Hirschmann ◽

Kristina Knaudt ◽

Florian Holsboer

Keyword(s):

Growth Hormone ◽

Slow Wave Sleep ◽

Hormone Secretion ◽

Growth Hormone Secretion ◽

Sleep Eeg ◽

Megestrol Acetate ◽

Simultaneous Administration ◽

Healthy Men ◽

Eeg Recordings ◽

Time Awake

Administration of steroid hormones was demonstrated to modulate the sleep electroencephalogram (EEG) and sleep-associated hormonal secretion in specific ways. The present study was conducted to compare the effects of mifepristone (Mif), a mixed glucocorticoid (GR) and progesterone receptor (PR) antagonist, and megestrol acetate (Meg), a PR agonist. Nine healthy men were pretreated with either placebo or 200 mg Mif or 320 mg Meg, or a combination of both. Changes in plasma adrenocorticotropic hormone (ACTH), cortisol, and growth hormone concentrations were registered every 30 min; sleep EEG recordings were obtained continuously. Administration of Mif increased the morning plasma ACTH and cortisol surges, whereas Meg had the opposite effect. Growth hormone secretion was lowered by Mif pretreatment and enhanced by Meg. Simultaneous administration of both compounds led to largely compensated effects. The sleep EEG changes induced by Mif were a slight increase in the time awake and a delayed onset of slow-wave sleep. Meg led to a reduction of rapid-eye-movement sleep. Simultaneous administration of Mif and Meg showed a synergism in increasing time awake and shallow sleep: it therefore may be concluded that the sleep EEG effects are mediated by an interaction of GR and PR in unknown mechanisms.

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