Sleep Stage Transitions Research Articles

0768 BAYESIAN NETWORK MODELING OF DAYTIME AND NIGHTTIME SLEEP ARCHITECTURE

The complex pattern of sleep stages over a night (sleep architecture) exhibits abnormal patterns in disorders such as apnea and depression. Quantifying variability in sleep architecture caused by benign versus harmful factors may provide a powerful diagnostic tool. Current measures of sleep architecture, such as stage proportions, fail to capture the dynamics of human sleep stage transitions. Here, we investigate the effect of individual (age, sex, BMI) and previous sleep factors (time of day, time spent in a stage) on alternate measures of sleep architecture: transition probabilities (the likelihood of transition from one state to another) and stage duration distributions. We fit various discrete Bayesian networks to large amounts of sleep data (>9000 nights/naps) to determine the relational structure between variables. We find, in accordance with the literature, that sex, age, but not BMI significantly influence sleep stage durations. Specifically, older men have shorter bouts (more fragmentation) of REM sleep (11% more bouts < 6 minutes) and Slow Wave Sleep (2% more bouts < 6 mins) compared to older women. Older males and females had more fragmented sleep across all stages, although this pattern was stronger for males (older vs younger females = 3%, older vs younger males = 5% more < 6 minutes). Interestingly, transition probabilities are unaffected by sex, age, and BMI. Additionally, we find that the identity of the next stage is dependent on only the last two states. These findings demonstrate low complexity in sleep stage transitions, which can be useful for future work on a higher resolution continuous Bayesian network capable of predicting sleep stage sequences. Future work will also use Bayesian network modeling to characterize differences between healthy and unhealthy sleep architecture patterns.

0659 FREQUENT CRAVING FOR SWEETS AT WAKE UP IS ASSOCIATED WITH SUBJECTIVE NOCTURNAL AWAKENING AND SLEEP STAGE TRANSITION INDEX IN PATIENTS WITH NARCOLEPSY TYPE 1

Body mass index independent metabolic abnormalities are reportedly associated with narcolepsy. We found that abnormal fatty acid metabolism is involved in the pathophysiology of narcolepsy and other hypersomnia, and appeared to be associated with the symptom of frequent nocturnal awakenings. We hypothesized that craving for sweets at wake up, often observed in narcolepsy patients, reflected the dysfunction of energy metabolism, and studied the possible association of this symptom with subjective and objective nocturnal sleep instability. Subjects were a series of consecutive patients who visited Seiwa hospital for unexplained excessive daytime sleepiness and underwent PSG-MSLT for diagnosis purpose from October 2014 to November 2016. All subjects gave written informed consent. Those comorbid with other sleep disorders and with severe first night effect on PSG were excluded. Finally data from 139 subjects (Narcolepsy type1 19, type2 28, idiopathic hypersomnia 65, non hypersomnia control 27) were used for analyses. Age(25.8 ± 8.8) and BMI(21.6 ± 2.7) did not differ among 4 groups. Demographic data and information related to sleep habits, including frequency of craving for sweets at wake up and frequency and duration of subjective nocturnal awakening were obtained by questionnaire. Nocturnal sleep instability was also evaluated objectively by calculating sleep stage transition index in addition to conventional sleep variables. Frequency of craving for sweets at wake up tended to be high in narcolepsy group but the disese specificity did not reach statistical significance. This symptom was associated with the number of nocturnal awakenings and total duration of nocturnal awakenings. It is also associated with sleep transition index such that sleep transition index is higher among those with frequent sweets craving group, especially in narcolepsy type1. Association of the frequency of craving for sweets and subjective/objective sleep instability suggests that this symptom could reflect the dysfunction in energy metabolism and contribute to the sleep instability. Further studies with larger sample size and with objective evaluation of energy status is required and ongoing. None.

Quantifying the dynamic of OSA brain using multifractal formalism: A novel measure for sleep fragmentation.

It is thought that the critical brain dynamics in sleep is modulated during frequent periods of wakefulness. This paper utilizes the capacity of EEG based scaling analysis to quantify sleep fragmentation in patients with obstructive sleep apnea. The scale-free (fractal) behavior refers to a state where no characteristic scale dominates the dynamics of the underlying process which is evident as long range correlations in a time series. Here, Multiscaling (multifractal) spectrum is utilized to quantify the disturbed dynamic of an OSA brain with fragmented sleep. The whole night multichannel sleep EEG recordings of 18 subjects were employed to compute and quantify variable power-law long-range correlations and singularity spectra. Based on this characteristic, a new marker for sleep fragmentation named ``scaling based sleep fragmentation'' was introduced. This measure takes into account the sleep run length and stage transition quality within a fuzzy inference system to improve decisions made on sleep fragmentation. The proposed index was implemented, validated with sleepiness parameters and compared to some common indexes including sleep fragmentation index, arousal index, sleep diversity index, and sleep efficiency index. Correlations were almost significant suggesting that the sleep characterizing measure, based on singularity spectra range, could properly detect fragmentations and quantify their rate. This method can be an alternative for quantifying the sleep fragmentation in clinical practice after being approved experimentally. Control of sleep fragmentation and, subsequently, suppression of excessive daytime sleepiness will be a promising outlook of this kind of researches.

Sleep spindle density in narcolepsy

BackgroundPatients with narcolepsy type 1 (NT1) show alterations in sleep stage transitions, rapid-eye-movement (REM) and non-REM sleep due to the loss of hypocretinergic signaling. However, the sleep microstructure has not yet been evaluated in these patients. We aimed to evaluate whether the sleep spindle (SS) density is altered in patients with NT1 compared to controls and patients with narcolepsy type 2 (NT2). MethodsAll-night polysomnographic recordings from 28 NT1 patients, 19 NT2 patients, 20 controls (C) with narcolepsy-like symptoms, but with normal cerebrospinal fluid hypocretin levels and multiple sleep latency tests, and 18 healthy controls (HC) were included. Unspecified, slow, and fast SS were automatically detected, and SS densities were defined as number per minute and were computed across sleep stages and sleep cycles. The between-cycle trends of SS densities in N2 and NREM sleep were evaluated within and between groups. ResultsBetween-group comparisons in sleep stages revealed no significant differences in any type of SS. Within-group analyses of the SS trends revealed significant decreasing trends for NT1, HC, and C between first and last sleep cycle. Between-group analyses of SS trends between first and last sleep cycle revealed that NT2 differ from NT1 patients in the unspecified SS density in NREM sleep, and from HC in the slow SS density in N2 sleep. ConclusionsSS activity is preserved in NT1, suggesting that the ascending neurons to thalamic activation of SS are not significantly affected by the hypocretinergic system. NT2 patients show an abnormal pattern of SS distribution.

Sleep–wake stability in narcolepsy patients with normal, low and unmeasurable hypocretin levels

ObjectiveTo compare diurnal and nocturnal electrophysiological data from narcolepsy patients with undetectable (<20 pg/mL), low (20–110 pg/mL) and normal (>110 pg/mL) cerebrospinal fluid (CSF) hypocretin-1 levels. Patients/MethodsA total of 109 narcolepsy patients and 37 controls were studied; all had available CSF hypocretin-1 measurements. The sleep laboratory studies were conducted between 2008 and 2014. The study retrospectively examined measurements of sleep stage transitions in diurnal and nocturnal continuous polysomnography. The percentage distribution of time awake and rapid eye movement (REM) sleep, and the occurrence of sleep onset REM (SOREM) in the nocturnal polysomnography were also measured. ResultsParticipants with undetectable hypocretin-1 levels had significantly higher frequencies of transitions than controls and those with normal hypocretin-1 levels. Participants with low hypocretin-1 levels showed more transitions than controls and, in some cases, also more than those with normal hypocretin-1. Participants with normal hypocretin-1 failed to show any significant difference from the controls, except in the overall diurnal transitions. ConclusionUndetectable hypocretin-1 levels in particular, but also low hypocretin-1 levels, were associated with a less stable phenotype featuring more sleep state transitions and SOREM episodes. In addition, there was a distinction between nocturnal and diurnal REM sleep in hypocretin-deficient participants, expressed as increased diurnal REM sleep, which was not reflected in nocturnal sleep.

HyCLASSS: A Hybrid Classifier for Automatic Sleep Stage Scoring.

Automatic identification of sleep stage is an important step in a sleep study. In this paper, we propose a hybrid automatic sleep stage scoring approach, named HyCLASSS, based on single channel electroencephalogram (EEG). HyCLASSS, for the first time, leverages both signal and stage transition features of human sleep for automatic identification of sleep stages. HyCLASSS consists of two parts: A random forest classifier and correction rules. Random forest classifier is trained using 30 EEG signal features, including temporal, frequency, and nonlinear features. The correction rules are constructed based on stage transition feature, importing the continuity property of sleep, and characteristic of sleep stage transition. Compared with the gold standard of manual scoring using Rechtschaffen and Kales criterion, the overall accuracy and kappa coefficient applied on 198 subjects has reached 85.95% and 0.8046 in our experiment, respectively. The performance of HyCLASS compared favorably to previous work, and it could be integrated with sleep evaluation or sleep diagnosis system in the future.

When a gold standard isn’t so golden: Lack of prediction of subjective sleep quality from sleep polysomnography

BackgroundReports of subjective sleep quality are frequently collected in research and clinical practice. It is unclear, however, how well polysomnographic measures of sleep correlate with subjective reports of prior-night sleep quality in elderly men and women. Furthermore, the relative importance of various polysomnographic, demographic and clinical characteristics in predicting subjective sleep quality is not known. We sought to determine the correlates of subjective sleep quality in older adults using more recently developed machine learning algorithms that are suitable for selecting and ranking important variables. MethodsCommunity-dwelling older men (n=1024) and women (n=459), a subset of those participating in the Osteoporotic Fractures in Men study and the Study of Osteoporotic Fractures study, respectively, completed a single night of at-home polysomnographic recording of sleep followed by a set of morning questions concerning the prior night’s sleep quality. Questionnaires concerning demographics and psychological characteristics were also collected prior to the overnight recording and entered into multivariable models. Two machine learning algorithms, lasso penalized regression and random forests, determined variable selection and the ordering of variable importance separately for men and women. ResultsThirty-eight sleep, demographic and clinical correlates of sleep quality were considered. Together, these multivariable models explained only 11–17% of the variance in predicting subjective sleep quality. Objective sleep efficiency emerged as the strongest correlate of subjective sleep quality across all models, and across both sexes. Greater total sleep time and sleep stage transitions were also significant objective correlates of subjective sleep quality. The amount of slow wave sleep obtained was not determined to be important. ConclusionsOverall, the commonly obtained measures of polysomnographically-defined sleep contributed little to subjective ratings of prior-night sleep quality. Though they explained relatively little of the variance, sleep efficiency, total sleep time and sleep stage transitions were among the most important objective correlates.

Nocturnal Hot Flashes: Relationship to Objective Awakenings and Sleep Stage Transitions.

While women report sleep interruption secondary to nighttime hot flashes, the sleep disrupting impact of nocturnal hot flashes (HF) is not well characterized. We utilized a model of induced HF to investigate the relationship of nighttime HF to sleep architecture and sleep-stage transitions. Twenty-eight healthy, premenopausal volunteers received the depot gonadotropin-releasing hormone agonist (GnRHa) leuprolide to rapidly induce menopause, manifesting with HF. Sleep disruption was measured on 2 polysomnograms conducted before and after 4-5 weeks on leuprolide, when HF had developed. 165 HF episodes were recorded objectively during 48 sleep studies (mean 3.4 HF/night). After standardizing to sleep-stage time distribution, the majority of HF were recorded during wake (51.0%) and stage N1 (18.8%). Sixty-six percent of HF occurred within 5 minutes of an awakening, with 80% occurring just before or during the awakening. Objective HF were not associated with sleep disruption as measured by increased transitions to wake or N1, but self-reported nocturnal HF correlated with an increase from pre- to post-leuprolide in the rate of transitions to wake (p = 0.01), and to N1 (p = 0.008). By isolating the effect of HF on sleep in women without the confound of age-related sleep changes associated with natural menopause, this experimental model shows that HF arise most commonly during N1 and wake, typically preceding or occurring simultaneously with wake episodes. Perception of HF, but not objective HF, is linked to increased sleep-stage transitions, suggesting that sleep disruption increases awareness of and memory for nighttime HF events. ClinicalTrials.gov Identifier: NCT01116401.

Decreased sleep stage transition pattern complexity in narcolepsy type 1.

To analyze the complexity of the nocturnal sleep stage sequence in central disorders of hypersomnolence (CDH), with the hypothesis that narcolepsy type 1 (NT1) might exhibit distinctive sleep stage sequence organization and complexity. Seventy-nine NT1 patients, 22 narcolepsy type 2 (NT2), 22 idiopathic hypersomnia (IH), and 52 patients with subjective hypersomnolence (sHS) were recruited and their nocturnal sleep was polysomnographically recorded and scored. Group between-stage transition probability matrices were obtained and compared. Patients with NT1 differed significantly from all the other patient groups, the latter, in turn, were not different between each other. The individual probability of the R-to-N2 transition was found to be the parameter showing the difference of highest significance between the groups (lowest in NT1) and classified patients with or without NT1 with an accuracy of 78.9% (sensitivity 78.5% and specificity 79.2%), by applying a cut-off value of 0.15. The main result of this study is that the structure of the sleep stage transition pattern of hypocretin-deficient NT1 patients is significantly different from that of other forms of CDH and sHS, with normal hypocretin levels. The lower probability of R-to-N2 transition occurrence in NT1 appears to be a reliable polysomnographic feature with potential application at the individual level, for supportive diagnostic purposes.

Sleep Hunter: Towards Fine Grained Sleep Stage Tracking with Smartphones

Sleep quality plays a vital role in personal health. A great deal of effort has been paid to design sleep quality monitoring systems, providing services ranging from bedtime monitoring to sleep activity detection. However, as sleep quality is closely related to the distribution of sleep duration over different sleep stages, neither the bedtime nor the intensity of sleep activities is able to reflect sleep quality precisely. We present Sleep Hunter, a mobile service that provides a fine-grained detection of sleep stage transition for sleep quality monitoring and intelligent wake-up call. The rationale is that each sleep stage is accompanied by specific body movements and acoustic signals. Leveraging the built-in sensors on smartphones, Sleep Hunter integrates these physical activities with sleep environment, inherent temporal relation, and personal factors by a statistical model for a fine-grained sleep stage detection. Based on the duration of each sleep stage, Sleep Hunter further provides sleep quality report and smart call service for users. Experimental results from over 30 sets of nocturnal sleep data show that our system is superior to existing actigraphy-based sleep quality monitoring systems, and achieves satisfying detection accuracy compared with dedicated polysomnography-based devices.

Cardiorespiratory Sleep Stage Detection Using Conditional Random Fields.

This paper explores the probabilistic properties of sleep stage sequences and transitions to improve the performance of sleep stage detection using cardiorespiratory features. A new classifier, based on conditional random fields, is used in different sleep stage detection tasks (N3, NREM, REM, and wake) in night-time recordings of electrocardiogram and respiratory inductance plethysmography of healthy subjects. Using a dataset of 342 polysomnographic recordings of healthy subjects, among which 135 with regular sleep architecture, it outperforms hidden Markov models and Bayesian linear discriminants in all tasks, achieving an average accuracy of 87.38% and kappa of 0.41 (87.27% and 0.49 for regular subjects) for N3 detection, 78.71% and 0.55 (80.34% and 0.56 for regular subjects) for NREM detection, 88.49% and 0.51 (87.35% and 0.57 for regular subjects) for REM, and 85.69% and 0.51 (90.42% and 0.52 for regular subjects) for wake. In comparison with the state of the art, and having been tested on a much larger dataset, the classifier was found to outperform most of the work reported in the literature for some of the tasks, in particular for subjects with regular sleep architecture. It achieves a comparable accuracy for N3, higher accuracy and kappa for REM, and higher accuracy and comparable kappa for NREM than the best performing classifiers described in the literature.

Labile sleep promotes awareness of abstract knowledge in a serial reaction time task.

Sleep has been identified as a critical brain state enhancing the probability of gaining insight into covert task regularities. Both non-rapid eye movement (NREM) and rapid eye movement (REM) sleep have been implicated with offline re-activation and reorganization of memories supporting explicit knowledge generation. According to two-stage models of sleep function, offline processing of information during sleep is sequential requiring multiple cycles of NREM and REM sleep stages. However, the role of overnight dynamic sleep macrostructure for insightfulness has not been studied so far. In the present study, we test the hypothesis that the frequency of interactions between NREM and REM sleep stages might be critical for awareness after sleep. For that aim, the rate of sleep stage transitions was evaluated in 53 participants who learned implicitly a serial reaction time task (SRTT) in which a determined sequence was inserted. The amount of explicit knowledge about the sequence was established by verbal recall after a night of sleep following SRTT learning. Polysomnography was recorded in this night and in a control night before and was analyzed to compare the rate of sleep-stage transitions between participants who did or did not gain awareness of task regularity after sleep. Indeed, individual ability of explicit knowledge generation was strongly associated with increased rate of transitions between NREM and REM sleep stages and between light sleep stages and slow wave sleep. However, the rate of NREM–REM transitions specifically predicted the amount of explicit knowledge after sleep in a trait-dependent way. These results demonstrate that enhanced lability of sleep goes along with individual ability of knowledge awareness. Observations suggest that facilitated dynamic interactions between sleep stages, particularly between NREM and REM sleep stages play a role for offline processing which promotes rule extraction and awareness.

Bradysomnia in Parkinson’s disease

ObjectivePolysomnography studies in Parkinson’s disease (PD) patients show altered sleep microstructure with decreased level of arousability, indicating impaired sleep–wake dynamics in PD. The aim of this study was to investigate dynamical aspects of sleep EEG in PD as compared to healthy controls. MethodsIn this retrospective, controlled study, we applied a previously established mathematical model of sleep EEG analysis (state space model) to PD patients and age- and gender-matched healthy volunteers (N=64). Dynamical aspects of sleep were quantified by measuring the spectral variability of the sleep EEG (by means of state space velocity). ResultsState space analysis revealed preserved global sleep–wake architecture in PD patients, but the velocity of sleep stage transitions was significantly reduced as compared to healthy controls. Correlation analysis revealed a strong association of state space velocity with arousal scores and daily dopamine agonist intake. ConclusionsQuantitative analysis of spectral sleep EEG variability (state space velocity) revealed reduced sleep–wake dynamics in PD patients as compared to control subjects. SignificanceWe propose state space velocity as an objective and quantitative measure for altered sleep microstructure and as a potential biomarker of sleep alterations in PD, not accessible by conventional sleep analysis.

Altered Sleep Stage Transitions of REM Sleep: A Novel and Stable Biomarker of Narcolepsy.

To determine the diagnostic values, longitudinal stability, and HLA association of the sleep stage transitions in narcolepsy. To compare the baseline differences in the sleep stage transition to REM sleep among 35 patients with type 1 narcolepsy, 39 patients with type 2 narcolepsy, 26 unaffected relatives, and 159 non-narcoleptic sleep patient controls, followed by a reassessment at a mean duration of 37.4 months. The highest prevalence of altered transition from stage non-N2/N3 to stage R in multiple sleep latency test (MSLT) and nocturnal polysomnography (NPSG) was found in patients with type 1 narcolepsy (92.0% and 57.1%), followed by patients with type 2 narcolepsy (69.4% and 12.8%), unaffected relatives (46.2% and 0%), and controls (39.3% and 1.3%). Individual sleep variables had varied sensitivity and specificity in diagnosing narcolepsy. By incorporating a combination of sleep variables, the decision tree analysis improved the sensitivity to 94.3% and 82.1% and enhanced specificity to 82.4% and 83% for the diagnosis of type 1 and type 2 narcolepsy, respectively. There was a significant association of DBQ1*0602 with the altered sleep stage transition (OR = 16.0, 95% CI: 1.7-149.8, p = 0.015). The persistence of the altered sleep stage transition in both MSLT and NPSG was high for both type 1 (90.5% and 64.7%) and type 2 narcolepsy (92.3% and 100%), respectively. Altered sleep stage transition is a significant and stable marker of narcolepsy, which suggests a vulnerable wake-sleep dysregulation trait in narcolepsy. Altered sleep stage transition has a significant diagnostic value in the differential diagnosis of hypersomnias, especially when combined with other diagnostic sleep variables in decision tree analysis.

Nocturnal Sleep Dynamics Identify Narcolepsy Type 1

To evaluate the reliability of nocturnal sleep dynamics in the differential diagnosis of central disorders of hypersomnolence. Cross-sectional. Sleep laboratory. One hundred seventy-five patients with hypocretin-deficient narcolepsy type 1 (NT1, n = 79), narcolepsy type 2 (NT2, n = 22), idiopathic hypersomnia (IH, n = 22), and "subjective" hypersomnolence (sHS, n = 52). None. Polysomnographic (PSG) work-up included 48 h of continuous PSG recording. From nocturnal PSG conventional sleep macrostructure, occurrence of sleep onset rapid eye movement period (SOREMP), sleep stages distribution, and sleep stage transitions were calculated. Patient groups were compared, and receiver operating characteristic (ROC) curve analysis was used to test the diagnostic utility of nocturnal PSG data to identify NT1. Sleep macrostructure was substantially stable in the 2 nights of each diagnostic group. NT1 and NT2 patients had lower latency to rapid eye movement (REM) sleep, and NT1 patients showed the highest number of awakenings, sleep stage transitions, and more time spent in N1 sleep, as well as most SOREMPs at daytime PSG and at multiple sleep latency test (MSLT) than all other groups. ROC curve analysis showed that nocturnal SOREMP (area under the curve of 0.724 ± 0.041, P < 0.0001), percent of total sleep time spent in N1 (0.896 ± 0.023, P < 0.0001), and the wakefulness-sleep transition index (0.796 ± 0.034, P < 0.0001) had a good sensitivity and specificity profile to identify NT1 sleep, especially when used in combination (0.903 ± 0.023, P < 0.0001), similarly to SOREMP number at continuous daytime PSG (0.899 ± 0.026, P < 0.0001) and at MSLT (0.956 ± 0.015, P < 0.0001). Sleep macrostructure (i.e. SOREMP, N1 timing) including stage transitions reliably identifies hypocretin-deficient narcolepsy type 1 among central disorders of hypersomnolence.

Time-dependent sleep stage transition model based on heart rate variability.

A new model is proposed to automatically classify sleep stages using heart rate variability (HRV). The generative model, based on the characteristics that the distribution and the transition probabilities of sleep stages depend on the elapsed time from the beginning of sleep, infers the sleep stage with a Gibbs sampler. Experiments were conducted using a public data set consisting of 45 healthy subjects and the model's classification accuracy was evaluated for three sleep stages: wake state, rapid eye movement (REM) sleep, and non-REM sleep. Experimental results demonstrated that the model provides more accurate sleep stage classification than conventional (naive Bayes and Support Vector Machine) models that do not take the above characteristics into account. Our study contributes to improve the quality of sleep monitoring in the daily life using easy-to-wear HRV sensors.

Sleep-stage transitions during polysomnographic recordings as diagnostic features of type 1 narcolepsy

ObjectiveType 1 narcolepsy/hypocretin deficiency is characterized by excessive daytime sleepiness, sleep fragmentation, and cataplexy. Short rapid eye movement (REM) latency (≤15 min) during nocturnal polysomnography (PSG) or during naps of the multiple sleep latency test (MSLT) defines a sleep-onset REM sleep period (SOREMP), a diagnostic hallmark. We hypothesized that abnormal sleep transitions other than SOREMPs can be identified in type 1 narcolepsy. MethodsSleep-stage transitions (one to 10 epochs to one to five epochs of any other stage) and bout length features (one to 10 epochs) were extracted from PSGs. The first 15 min of sleep were excluded when a nocturnal SOREMP was recorded. F0.1 measures and receiver operating characteristic curves were used to identify specific (≥98%) features. A data set of 136 patients and 510 sex- and age-matched controls was used for the training. A data set of 19 cases and 708 sleep-clinic patients was used for the validation. Results(1) ≥5 transitions from ≥5 epochs of stage N1 or W to ≥2 epochs of REM sleep, (2) ≥22 transitions from ≥3 epochs of stage N2 or N3 to ≥2 epochs of N1 or W, and (3) ≥16 bouts of ≥6 epochs of N1 or W were found to be highly specific (≥98%). Sensitivity ranged from 16% to 30%, and it did not vary substantially with and without medication or a nocturnal SOREMP. In patients taking antidepressants, nocturnal SOREMPs occurred much less frequently (16% vs. 36%, p < 0.001). ConclusionsIncreased sleep-stage transitions notably from ≥2.5 min of W/N1 into REM are specifically diagnostic for narcolepsy independent of a nocturnal SOREMP.

Prognosis in Relation to Blood Pressure Variability

Prognosis in Relation to Blood Pressure Variability

Changes of sleep-stage transitions due to ageing and sleep disorder.

Transition patterns between different sleep stages are analysed in terms of probability distributions of symbolic sequences for young and old subjects with and without sleep disorder. Changes of these patterns due to ageing are compared with variations of transition probabilities due to sleep disorder.

PO-0473 Caffeine Treatment Has No Significant Effect On Sleep Quality In Preterm Infants: Abstract PO-0473 Table 1

Background and aims Sleep stages begin to emerge from 20 weeks’ gestational age. Typical EEG changes appear at 28–30 weeks with periods of rapid eye movement (REM) and non-REM (NREM) sleep. Sleep patterns develop into near adult like patterns by 3–5 months of age. Sleep cycles (REM/NREM) are essential for infant brain development. Caffeine is routinely used for treatment and prevention of apnea of prematurity. Its effect on sleep organisation in preterm infants has been controversial. This study aimed to find differences in sleep organisation in preterm infants before and after initiation of caffeine treatment. Methods Polysomnography was recorded in 10 preterm infants [GA 27+2–36+6 (mean 30+1) weeks, BW 790–2875 (mean 1465) g] at 34–41 (avg. 36) weeks’ GA before and the day after administration of caffeine citrate loading dose (20 mg/kg). The analysis was done with visual scoring. Results Sleep quality was variable. Some infants had very interrupted sleep structure. Uninterrupted phasic REM sleep periods seemed to be more readily identified after caffeine. However, we were unable to show this by using standard indexes. There was no difference in sleep stage distribution, number of awakenings, number of sleep stage transitions or average REM period (Table 1). Conclusions We did not find any evident effect of caffeine on sleep quality in preterm infants.