ライフサイエンスコーパス: sleep stage

1 processes occurring during this paradoxical sleep stage.

2 incoming information, and contingent on the sleep stage.

3 en during quiescence that indicates a deeper sleep stage.

4 threshold, representing a distinct 'active' sleep stage.

5 entral apnoeas are less frequent during this sleep stage.

6 ity regulate the intensity of the first deep sleep stage.

7 t varied because of sound level and type and sleep stage.

8 p efficiency, and percentage of time in each sleep stage.

9 al distinct types of activity changes across sleep stages.

10 val and RR variability increased through all sleep stages.

11 leptiform discharges (IEDs) across different sleep stages.

12 remained stable from wakefulness through all sleep stages.

13 strongly activated during nonREM and/or REM sleep stages.

14 no difference in Pcrit was detected between sleep stages.

15 hy (PSG) and expert manual classification of sleep stages.

16 es of microbehaviors associated with certain sleep stages.

17 vity, which reliably discriminates different sleep stages.

18 embling into cell networks tuned to specific sleep stages.

19 nd 96.14%, respectively for 5, 3, and binary sleep stages.

20 utside the lab, including timing of specific sleep stages.

21 sals, sleep spindles and transitions between sleep stages.

22 p technology appeared to accurately quantify sleep stages.

23 alone could be used to differentiate between sleep stages.

24 rmal physiological changes across those same sleep stages.

25 transition probabilities, beyond PSG-defined sleep stages.

26 rived participants who reached all PSG-based sleep stages.

27 architecture, which includes non-REM and REM sleep stages.

28 it is unclear if invertebrates have distinct sleep stages.

29 astically different gating mechanisms across sleep stages.

30 ic brain EEG rhythms and transitions between sleep stages.

31 s system (ANS) shows strong variation across sleep stages.

32 been inconsistently observed in the various sleep stages.

33 p transients and spectral content during all sleep stages.

34 rapid eye movement (REM) and non-REM (NREM) sleep stages.

35 or documented homeostatic regulation of both sleep stages.

36 he range of normal hearing]) during specific sleep stages.

37 the SCN can time the occurrence of specific sleep stages.

38 gue-Dawley rats chronically instrumented for sleep staging.

39 gue-Dawley rats chronically instrumented for sleep staging.

40 ening mechanics, heart rate variability, and sleep staging.

41 age 36-50 years) and was replaced by lighter sleep (stages 1 and 2) without significant increases in

42 ly lower in children with SDB during non-REM sleep (stage 2: P = 0.03; slow-wave sleep: P = 0.001).

43 ment (REM)-sleep, total sleeping time (TST), sleep stage 2 (S2), and QS [(SWS + REM) / TST x 100%] we

44 eye movement (REM) sleep and non-REM (NREM) sleep stages 2 and 3.

45 hat blunted hypoxic arousal responses during sleep Stage 3/4 would be present in PWS.

46 effects (e.g. memory impairment, increase in sleep stages 3 and 4, dependence, seizures and coma) tha

47 o elicit absence seizures and an increase in sleep stages 3 and 4, have been clarified.

48 ance of HRV and SKNA for classification of 5 sleep stages, 3 stages and binary stages.

49 oxygen saturation (SpO(2)), and EEG improved sleep staging accuracy.

50 This tool offers high sleep-staging accuracy that matches human scoring accura

51 th groups, nonrandom HEP were present in all sleep stages analyzed; however, amplitude of HEP were si

52 ed polysomnographic technologist live-scored sleep stage and administered stimuli on randomized count

53 Sleep stage and anatomic localization were tested as mod

54 fter an abrupt decrease in PN, regardless of sleep stage and despite an increase in genioglossus-musc

55 en sleep stages and energy expenditure, with sleep stage and overnight energy expenditure patterns ta

56 al long short-term memory models to classify sleep stages and detect apnea events automatically.

57 Accurate identification of sleep stages and disorders is crucial for maintaining he

58 of energy balance, yet the relation between sleep stages and energy expenditure remains unclear.

59 tive was to investigate the relation between sleep stages and energy expenditure, with sleep stage an

60 e mechanisms controlling transitions between sleep stages and how they are synchronized with infraslo

61 as a key for interpreting the physiology of sleep stages and reconciling inconsistencies in terminol

62 As such, naps may contain mostly light sleep stages and serve little function for learning and

63 ccuracy = 97%; F1 score = 96%) in predicting sleep stages and showed robust performance even with a s

64 h thus limit their precision in detection of sleep stages and sleep disorders.

65 fMRI studies were too short to capture full sleep stages and their cycling.

66 ledge of the physiology of NREM and then REM sleep stages and their ordered succession.

67 nd seizure risk in addition to modulation by sleep stages and transitions between stages.

68 he revised scoring scheme proved reliable in sleep staging and may serve as a building block in futur

69 ythms; a mechanism essential for spontaneous sleep-stage and arousal transitions that lays the bases

70 al for the micro-architecture of spontaneous sleep-stage and arousal transitions within a novel, non-

71 ly reported sleep features (e.g., minutes in sleep stages) and changes in memory performance show con

72 tivity (SKNA) has been shown to vary between sleep stages, and it can be recorded simultaneously with

73 ociations between movement behaviors and nap sleep stages, and no effects for nap condition or condit

74 e anatomical and physiological correlates of sleep stages, and thus dreaming, allow a better understa

75 ement [REM] sleep latencies, non-REM and REM sleep stages, and wakefulness after sleep onset); and Mi

76 generate large amounts of data that require sleep stage annotation (polysomnography), in which the d

77 to accurately distinguish between important sleep stages are difficult and impractical to use with c

78 Our results advance the understanding of how sleep stages are genetically defined.

79 onvincing evidence that, in humans, discrete sleep stages are important for daytime brain function, b

80 as well as the performance across different sleep stages are on par with that of the human experts.

81 show that the complex micro-architecture of sleep-stage/arousal transitions arises from intrinsic no

82 We visually marked sleep stages, arousals, seizures, and epileptic bursts i

83 ates of reduced arousal, including different sleep stages as well as anesthesia in humans.

84 We find a transitional sleep stage associated with a 7-10 Hz oscillation in the

85 We confirm the existence of a distinct sleep stage associated with rhythmic proboscis extension

86 r these effects depend on circadian phase or sleep stage at awakening.

87 ckage with a graphic user interface to score sleep stages automatically in mice.

88 The overall accuracy in sleep staging between WP and PSG was 62% with Kappa agre

89 es in body temperature and EEG recordings of sleep stage bouts.

90 WS), is thought to be the most "restorative" sleep stage, but beneficial effects of SWS for physical

91 Small differences in EE were observed among sleep stages, but wakefulness during the sleep episode w

92 namely HybridAtt, to automatically classify sleep stages by capturing channel and temporal correlati

93 In this study, we propose that sleep stages can be classified using features derived fr

94 al inspection of PSG, automatic multivariate sleep stage classification has become an important resea

95 arison of SlumberNet's performance to manual sleep stage classification revealed a significant reduct

96 In line with literature, sleep stage classification turned out to be difficult us

97 ep learning have shown promise in automating sleep stage classification using a single PSG channel.

98 efficient, accurate, and scalable method for sleep stage classification.

99 he effectiveness of HybridAtt in the task of sleep stage classification.

100 These sleep stage classifications have been based on convenien

101 developed a machine learning-based automated sleep stage classifier in a cohort of 25 preterm infants

102 a lower level of irreversibility in the deep sleep stage, compared to the other.

103 TN LFP features that characterised different sleep stages, correlated with arousal and sleep fragment

104 No slow wave sleep or rapid eye movement sleep stages could be identified and no homoeostatic reg

105 ularities, ranging from circadian rhythms to sleep stage cycles and neuronal oscillations during nonr

106 Sleep stages, cyclic alternating pattern (CAP), and leg

107 put channels, making it adaptable to diverse sleep staging datasets.

108 portions of the signals support our model's sleep stage decision, and we verified that these portion

109 an 8-h night of sleep in terms of magnitude, sleep-stage dependency and retinotopic specificity, and

110 ultradian sleep states to determine whether sleep-stage dependent spectral patterns might reflect un

111 that aperiodic activity reliably dissociated sleep stage-dependent dynamics in a regionally specific

112 ntly reduced cigarette-smoking behavior in a sleep stage-dependent manner, and this effect persisted

113 ion, arousals from sleep, and alterations in sleep stage distribution.

114 activation during non-REM sleep (EE-SWAS), a sleep stage dominated by sleep spindles, which are brain

115 iciency, or slow-wave and rapid eye movement sleep stage duration (P > .30).

116 These findings show that sleep stages, duration and regularity are all important

117 gram, we show that sleep patterns, including sleep stages, duration and regularity, are associated wi

118 quantifying sleep/wake states as compared to sleep staging durations.

119 We also determined effects of sleep stage during baseline and recovery sleep on EE.

120 nd ultradian patterns of secretion) data and sleep-staged EEG from healthy human participants.

121 Finally, during deeper sleep stages, electroencephalographic analysis revealed

122 lts show that the difference in CRPS between sleep stages exceeds the difference between young and el

123 Sleep stages exerted no effect on energy expenditure dur

124 x remains highly active during the different sleep stages, exhibiting complex interactions between di

125 nergy expenditure was calculated during each sleep stage for the whole night and separately for sleep

126 bject level and group level across different sleep stages for duration and distribution of IEDs.

127 radient boosting machine (LightGBM) to score sleep stages for each epoch of recordings.

128 orks and random forest algorithms to predict sleep stages from 15 variables (features) of the muscle

129 Scoring sleep stages from biological signals is an essential but

130 the data and were able to accurately predict sleep stages from HR and muscle activity alone with clas

131 Slow wave sleep (SWS), the deepest sleep stage hallmarked by electroencephalographic slow o

132 e brain function, but whether any particular sleep stage has functional significance for the rest of

133 [5], but its role in the timing of specific sleep stages has remained unknown.

134 n tethered flies to identify a discrete deep sleep stage in Drosophila, termed proboscis extension sl

135 ppears to give an accurate representation of sleep stages in cattle and could consequentially enable

136 s determine the timing and intensity of deep sleep stages in Drosophila.

137 Identifying different sleep stages in humans and other mammals has traditional

138 Evidence for different sleep stages in invertebrates remains elusive, even thou

139 (PFC) activity recorded across behavior and sleep stages in male rats learning a spatial alternation

140 is maintained across non-rapid eye movement sleep stages in subcortical nuclei, yet decreases in dee

141 functions of intermittent transitions among sleep stages, including brief awakenings and arousals, c

142 functions of intermittent transitions among sleep stages, including short awakenings and arousals, c

143 However, how these two sleep stages influence each other and thereby regulate t

144 igational memory, but the role of particular sleep stages is less clear.

145 tterns and how they change between different sleep stages is presently unknown.

146 to a simultaneous increase in the amount of sleep stage IV.

147 is a well-established marker of arousal and sleep stages measured using electroencephalography.

148 vided into five sections: (1) an overview of sleep stages, memory categories, and the distinct stages

149 To integrate a sleep staging method into clinical practice effectively,

150 Sleep and wake summary outcomes as well as sleep staging metrics were evaluated, where available, f

151 cy of this compound to suppress apnea in all sleep stages most probably arises from its mixed agonist

152 physiological events that characterize these sleep stages must mediate sleep-dependent memory process

153 local mismatch response remained across all sleep stages (N1, N2, and REM sleep), but with an incomp

154 Sections during non-rapid eye movement sleep (stages N2 and N3) and rapid eye movement sleep (s

155 these tCS patterns was then reapplied during sleep stages N2 and SWS coupled to slow oscillations in

156 shorter REM latency, lower levels of non-REM sleep (stage N3), and reduced delta power during daytime

157 main findings were an increase in nocturnal sleep stage N3 (7.5 +/- 21.6 min/7.5 h, mean +/- SD; p =

158 Amyloid-beta fluctuations were modeled with sleep stages, (non)oscillatory power, and hormones as pr

159 s been applied in recent years to categorize sleep stages (NREM, REM, and wake) using electroencephal

160 R periods of the night, no overall effect of sleep stage on energy expenditure, except for WASO compa

161 On the second night, the effect of sleep stage on pressure-flow relationships of the upper

162 hesis that there is a differential effect of sleep stage on QT interval in women compared with men.

163 On the first night, the effect of sleep stage on the relationship of retropalatal cross-se

164 We examined the effects of the various sleep stages on RR and QT intervals in healthy subjects

165 to baseline and postarousal, irrespective of sleep stage or brain area.

166 nergy expenditure does not vary according to sleep stage overnight, except for higher energy expendit

167 ed by 15 +/- 5% with zolpidem throughout all sleep stages (p = 0.010), whereas genioglossus muscle re

168 Coincidence of the sleep stage pattern and the overnight energy expenditure

169 Finally, sleep stages per se do not affect memories.

170 ep (stages N2 and N3) and rapid eye movement sleep (stage R) were selected from the first sleep cycle

171 ebrate groups alternate between at least two sleep stages: rapid eye movement and slow wave sleep(1-4

172 s similar performance to the HRV for 2 and 3 sleep stages recognition.

173 2.36 +/- 1.08 bpm, mean +/- SD; p < 0.0001); sleep stage REM did not change (p = 0.3564).

174 the molecular pathways underlying different sleep stages remain unclear.

175 , although their relationship to EEG-defined sleep stages remains unknown.

176 Sleep stages resembling the REM and non-REM (NREM) phase

177 r state-of-the-art performance for automatic sleep stage scoring in mice.

178 propose a method based on ensemble of small sleep stage scoring models with different input signal s

179 matic signals simultaneously, making in-home sleep stage scoring systems more suitable for clinical p

180 Sleep stage sequence analysis of SOREM periods may also

181 We performed sleep stage sequence analysis on 127 patients with noctu

182 scriptors [wake after sleep onset, number of sleep stage shifts, and lowest oxyhemoglobin saturation

183 Energy expenditure and sleep stages showed characteristic patterns during the n

184 d adoption of an industry-standard automated sleep staging software package.

185 duced marked, dose-responsive disruptions in sleep stage-specific EEG spectral profiles compared with

186 be coordinated between these structures in a sleep stage-specific manner.

187 ng mice, we reveal and characterize multiple sleep stage-specific physiological mechanisms linking th

188 e of different non-rapid eye movement (NREM) sleep stages (stages 2 and 3-4) with REM and while awake

189 However, the sleep-stage stratification pattern we uncover in CRPS do

190 ipples, classically associated with distinct sleep stages, supports the notion that a global coordina

191 results provide evidence of a discrete deep sleep stage that is linked to a specific function and su

192 n/KOR signalling affects transitions between sleep stages that promote REM sleep.

193 ement (REM) and nonrapid eye movement (NREM) sleep stages, that VIP neurons were most active during R

194 characteristics; however, within each single sleep stage, the functional state of the brain is contin

195 d this idea forward and examined, across all sleep stages, the brain's ability to flexibly process se

196 (awake stage), and subsequent consolidation (sleeping stage) to examine the contributions of each reg

197 ound that either the dynamics of cortisol or sleep stage transition, or a combination of both, could

198 he potential to both facilitate the study of sleep stage transitions and offer new insights into the

199 n healthy subjects dramatically changes with sleep-stage transitions and exhibits a pronounced strati

200 amic through a Hidden Markov Model, the deep sleep stage was revealed to have a lower switching rate

201 EEG spectral frequency power within specific sleep stages was calculated in 1-Hz intervals from 1 to

202 prevalent, but the distribution of classical sleep stages was similar between groups.

203 entation, as assessed by the distribution of sleep stages, was also an independent predictor of hyper

204 /min water into the proximal esophagus after sleep stages were confirmed.

205 Differences in sleep stages were not associated with cognition.

206 (Tvol) to trigger EUCR and 2P and changes in sleep stages were recorded during injection of 2.7 mL/mi

207 Sleep stages were scored visually for 20-second epochs;

208 Although sleep efficiency and proportions of sleep stages were within the normal range, sleep archite

209 he model has a non-rapid eye movement (NREM) sleep stage, where dynamics between the hippocampus and

210 delity versions of new attractors, and a REM sleep stage, where neocortex is able to more freely expl

211 Unresponsive anesthetic states and verified sleep stages, where a subsequent report of mental conten

212 e find that alternating between NREM and REM sleep stages, which alternately focuses the model's repl

213 ories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of

214 tion of sleep also determines which specific sleep stage will be manifested, and the circadian proces