Sleep-stage sequencing of sleep-onset REM periods in MSLT predicts treatment response in patients with narcolepsy

2015 ◽  
Vol 25 (2) ◽  
pp. 203-210 ◽  
Author(s):  
Panagis Drakatos ◽  
Kishankumar Patel ◽  
Chiraag Thakrar ◽  
Adrian J. Williams ◽  
Brian D. Kent ◽  
...  
Keyword(s):  
Treatment Response ◽  
Sleep Stage ◽  
Sleep Onset

Sleep Stage Assessment Using Power Spectral Indices of Heart Rate Variability With a Simple Algorithm

2012 ◽  
Vol 15 (3) ◽  
pp. 264-272 ◽  
Author(s):  
Keiko Tanida ◽  
Masashi Shibata ◽  
Margaret M. Heitkemper
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Low Frequency ◽  
Simple Algorithm ◽  
Sleep Stages ◽  
Spectral Indices ◽  
Current Form ◽  
Power Spectral

Clinical researchers do not typically assess sleep with polysomnography (PSG) but rather with observation. However, methods relying on observation have limited reliability and are not suitable for assessing sleep depth and cycles. The purpose of this methodological study was to compare a sleep analysis method based on power spectral indices of heart rate variability (HRV) data to PSG. PSG and electrocardiography data were collected synchronously from 10 healthy women (ages 20–61 years) over 23 nights in a laboratory setting. HRV was analyzed for each 60-s epoch and calculated at 3 frequency band powers (very low frequency [VLF]-hi: 0.016–0.04 Hz; low frequency [LF]: 0.04–0.15 Hz; and high frequency [HF]: 0.15–0.4 Hz). Using HF/(VLF-hi + LF + HF) value, VLF-hi, and heart rate (HR) as indices, an algorithm to categorize sleep into 3 states (shallow sleep corresponding to Stages 1 & 2, deep sleep corresponding to Stages 3 & 4, and rapid eye movement [REM] sleep) was created. Movement epochs and time of sleep onset and wake-up were determined using VLF-hi and HR. The minute-by-minute agreement rate with the sleep stages as identified by PSG and HRV data ranged from 32 to 72% with an average of 56%. Longer wake after sleep onset (WASO) resulted in lower agreement rates. The mean differences between the 2 methods were 2 min for the time of sleep onset and 6 min for the time of wake-up. These results indicate that distinguishing WASO from shallow sleep segments is difficult using this HRV method. The algorithm's usefulness is thus limited in its current form, and it requires additional modification.

The sleep macroarchitecture of children at risk for depression recruited in sleep centers

2012 ◽  
Vol 28 (3) ◽  
pp. 168-173 ◽  
Author(s):  
F. Bat-Pitault ◽  
D. Da Fonseca ◽  
S. Cortese ◽  
Y. Le Strat ◽  
L. Kocher ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
High Risk ◽  
Children And Adolescents ◽  
Depressive Episode ◽  
Risk Group ◽  
Sleep Stage ◽  
Sleep Onset ◽  
High Risk Group ◽  
Sleep Efficiency ◽  
History Of

AbstractObjectiveThe primary aim of this study was to compare the sleep macroarchitecture of children and adolescents whose mothers have a history of depression with children and adolescents whose mothers do not.MethodPolysomnography (PSG) and Holter electroencephalogram (EEG) were used to compare the sleep architecture of 35 children whose mothers had at least one previous depressive episode (19 boys, aged 4–18 years, “high-risk” group) and 25 controls (13 males, aged 4–18 years, “low-risk” group) whose mothers had never had a depressive episode. The total sleep time, wakefulness after sleep onset (WASO), sleep latency, sleep efficiency, number of awakenings per hour of sleep, percentages of time spent in each sleep stage, rapid eye movement (REM) latency and the depressive symptoms of participants were measured.ResultsIn children (4–12 years old), the high-risk group exhibited significantly more depressive symptoms than controls (P = 0.02). However, PSG parameters were not significantly different between high-risk children and controls. In adolescents (13–18 years old), the high-risk subjects presented with significantly more depressive symptoms (P = 0.003), a significant increase in WASO (P = 0.019) and a significant decrease in sleep efficiency compared to controls (P = 0.009).ConclusionThis study shows that children and adolescents born from mothers with a history of at least one depressive episode had significantly more depressive symptoms than controls. However, only high-risk adolescents presented with concurrent alterations of sleep macroarchitecture.

Utility of Activity Monitors and Thermometry in Assessing Sleep Stages and Sleep Quality

2018 ◽  
Vol 1 (3) ◽  
pp. 108-121
Author(s):  
Natashia Swalve ◽  
Brianna Harfmann ◽  
John Mitrzyk ◽  
Alexander H. K. Montoye
Keyword(s):  
Sleep Quality ◽  
Skin Temperature ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Sleep Stages ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Activity Monitors ◽  
Research Grade

Activity monitors provide an inexpensive and convenient way to measure sleep, yet relatively few studies have been conducted to validate the use of these devices in examining measures of sleep quality or sleep stages and if other measures, such as thermometry, could inform their accuracy. The purpose of this study was to compare one research-grade and four consumer-grade activity monitors on measures of sleep quality (sleep efficiency, sleep onset latency, and wake after sleep onset) and sleep stages (awake, sleep, light, deep, REM) against an electroencephalography criterion. The use of a skin temperature device was also explored to ascertain whether skin temperature monitoring may provide additional data to increase the accuracy of sleep determination. Twenty adults stayed overnight in a sleep laboratory during which sleep was assessed using electroencephalography and compared to data concurrently collected by five activity monitors (research-grade: ActiGraph GT9X Link; consumer-grade: Fitbit Charge HR, Fitbit Flex, Jawbone UP4, Misfit Flash) and a skin temperature sensor (iButton). The majority of the consumer-grade devices overestimated total sleep time and sleep efficiency while underestimating sleep onset latency, wake after sleep onset, and number of awakenings during the night, with similar results being seen in the research-grade device. The Jawbone UP4 performed better than both the consumer- and research-grade devices, having high levels of agreement overall and in epoch-by-epoch sleep stage data. Changes in temperature were moderately correlated with sleep stages, suggesting that addition of skin temperature could increase the validity of activity monitors in sleep measurement.

scholarly journals Excessive Fragmentary Myoclonus: Time of Night and Sleep Stage Distributions

1993 ◽  
Vol 20 (2) ◽  
pp. 142-146 ◽  
Author(s):  
Otavio Lins ◽  
Michelle Castonguay ◽  
Wayne Dunham ◽  
Sonya Nevsimalova ◽  
Roger Broughton
Keyword(s):  
Sleep Disorders ◽  
Patient Group ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Sleep Fragmentation ◽  
New Technique ◽  
A New Technique

ABSTRACT:Excessive fragmentary myoclonus during sleep consists of high amounts of brief twitch-like movements occurring asynchronously and asymmetrically in different body areas and has been reported to occur in association with a number of sleep disorders. It was analyzed using a new technique of quantification, the fragmentary myoclonus index (FMI). The FMI exhibited high rates in all stages of sleep but with a somewhat lower frequency in slow wave sleep explaining, as well, a significantly lower rate in the first hour after sleep onset compared to later hours. There was no evidence for greater sleep fragmentation or lighter sleep compared to a matched patient group in whom it had not been noted.

First rapid eye movement sleep periods and sleep-onset rapid eye movement periods in sleep-stage sequencing of hypersomnias

2013 ◽  
Vol 14 (9) ◽  
pp. 897-901 ◽  
Author(s):  
Panagis Drakatos ◽  
Christopher A. Kosky ◽  
Sean E. Higgins ◽  
Rexford T. Muza ◽  
Adrian J. Williams ◽  
...  
Keyword(s):  
Eye Movement ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep

Sleep stage sequence analysis of sleep onset REM periods in the hypersomnias

2012 ◽  
Vol 84 (2) ◽  
pp. 223-227 ◽  
Author(s):  
Panagis Drakatos ◽  
Angula Suri ◽  
Sean E Higgins ◽  
Irshaad O Ebrahim ◽  
Rexford T Muza ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Sleep Stage ◽  
Sleep Onset

The temporal relation between high release and sleep stage changes at nocturnal sleep onset in man

Life Sciences ◽  
1972 ◽  
Vol 11 (12) ◽  
pp. 587-593 ◽  
Author(s):  
Michael A. Pawel ◽  
Jon F. Sassin ◽  
Elliot D. Weitzman
Keyword(s):  
Sleep Stage ◽  
Sleep Onset ◽  
Temporal Relation ◽  
Nocturnal Sleep ◽  
High Release

scholarly journals 1210 Development Of An Auditory Neurofeedback During Sleep Onset Process

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A462-A463
Author(s):  
M Douch ◽  
M Soubrier ◽  
C Pinaud ◽  
M Harris ◽  
V Thorey
Keyword(s):  
Neural Network ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Sleep Onset Latency ◽  
The Neural Network ◽  
Binary State ◽  
Theta Frequency ◽  
Sleep Stage Classification ◽  
Onset Period ◽  
Current Stage

Abstract Introduction Biofeedback is proposed as an alternative method to help patients with insomnia reducing their anxiety. Some studies have shown that auditory neurofeedback can be effective at reducing sleep-onset latency. However, the AASM sleep stage classification only describes the sleep-onset as a binary state (i.e. wake or N1) which makes it not adapted for neurofeedback. We introduced a simple 4-stages classification for sleep-onset, on 10 seconds EEG epoch. The aim of this study was to develop an automatic method to detect these stages, and an online algorithm embedded in the Dreem headband (DH) that adapted the auditory feedback based on the current stage. Methods Fourteen subjects underwent an overnight PSG monitoring, from which the first sleep-onset period was extracted. We defined the simple 4-stages classification for sleep-onset on 10 seconds EEG epoch as following: SO1) > 75% of the epoch covered by alpha frequencies SO2) between 25% and 50% of the window covered with alpha frequencies, SO3) Alpha frequencies covered less than 25% and theta frequencies covered less than 30% of the epoch, and SO4) Theta frequency covered more than 30% of the epoch. For the manual scoring, 4 sleep scorers have been given the instructions and a Q&A session after scoring the first two records. For the algorithm, a sound triggering algorithm was linked to a neural network trained on the scored data, to dynamically adapt the sound to the sleep-onset stage. Results The scorers reached an average agreement of 68 + 15% over all the records. The neural network reached an accuracy of 68%. Per state the accuracy was: 71 ± 32% (S1), 52 ± 22% (S2), 54 ± 23% (S3), 79 ± 21% (S4). The automatic neurofeedback was able to adapt sound stimulations in real-time based on stages and was well perceived among first testers. Conclusion The results of this preliminary work show that we can reach a higher agreement by reducing the epoch duration and use this classification to produce automatic biofeedback during the sleep onset period. Further studies using a data-driven method should be conducted. Support This study supported by Dreem sas.

Thermoregulation, metabolism, and stages of sleep in cold-exposed men

1986 ◽  
Vol 61 (3) ◽  
pp. 940-947 ◽  
Author(s):  
J. W. Palca ◽  
J. M. Walker ◽  
R. J. Berger
Keyword(s):  
Ambient Temperature ◽  
Rem Sleep ◽  
Sleep Stage ◽  
Sleep Onset ◽  
O2 Consumption ◽  
Peripheral Vasoconstriction ◽  
Stage 2 Sleep

Four naked men, selected for their ability to sleep in the cold, were exposed to an ambient temperature (Ta) of 21 degrees C for five consecutive nights. Electrophysiological stages of sleep, O2 consumption (VO2), and skin (Tsk), rectal (Tre), and tympanic (Tty) temperatures were recorded. Compared with five nights at a thermoneutral Ta of 29 degrees C, cold induced increased wakefulness and decreased stage 2 sleep, without significantly affecting other stages. Tre and Tty declined during each condition. The decrease in Tre was greater at 21 degrees C than at 29 degrees C, whereas Tty did not differ significantly between conditions. Increases in Tty following REM sleep onset at 21 degrees C were negatively correlated with absolute Tty. VO2 and forehead Tsk also increased during REM sleep at both TaS, whereas Tsk of the limb extremities declined at 21 degrees C. Unsuppressed REM sleep in association with peripheral vasoconstriction and increased Tty and VO2 in cold-exposed humans, do not signify an inhibition of thermoregulation during this sleep stage as has been observed in other mammals.

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