A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidation.

How do we build up our knowledge of the world over time? Many theories of memory formation and consolidation have posited that the hippocampus stores new information, then "teaches" this information to the neocortex over time, especially during sleep. But it is unclear, mechanistically, how this actually works-How are these systems able to interact during periods with virtually no environmental input to accomplish useful learning and shifts in representation? We provide a framework for thinking about this question, with neural network model simulations serving as demonstrations. The model is composed of hippocampus and neocortical areas, which replay memories and interact with one another completely autonomously during simulated sleep. Oscillations are leveraged to support error-driven learning that leads to useful changes in memory representation and behavior. The model has a non-rapid eye movement (NREM) sleep stage, where dynamics between the hippocampus and neocortex are tightly coupled, with the hippocampus helping neocortex to reinstate high-fidelity versions of new attractors, and a REM sleep stage, where neocortex is able to more freely explore existing attractors. We find that alternating between NREM and REM sleep stages, which alternately focuses the model's replay on recent and remote information, facilitates graceful continual learning. We thus provide an account of how the hippocampus and neocortex can interact without any external input during sleep to drive useful new cortical learning and to protect old knowledge as new information is integrated.

The neurophysiology of closed-loop auditory stimulation in sleep: A magnetoencephalography study.

Closed-loop auditory stimulation (CLAS) is a brain modulation technique in which sounds are timed to enhance or disrupt endogenous neurophysiological events. CLAS of slow oscillation up-states in sleep is becoming a popular tool to study and enhance sleep's functions, as it increases slow oscillations, evokes sleep spindles and enhances memory consolidation of certain tasks. However, few studies have examined the specific neurophysiological mechanisms involved in CLAS, in part because of practical limitations to available tools. To evaluate evidence for possible models of how sound stimulation during brain up-states alters brain activity, we simultaneously recorded electro- and magnetoencephalography in human participants who received auditory stimulation across sleep stages. We conducted a series of analyses that test different models of pathways through which CLAS of slow oscillations may affect widespread neural activity that have been suggested in literature, using spatial information, timing and phase relationships in the source-localized magnetoencephalography data. The results suggest that auditory information reaches ventral frontal lobe areas via non-lemniscal pathways. From there, a slow oscillation is created and propagated. We demonstrate that while the state of excitability of tissue in auditory cortex and frontal ventral regions shows some synchrony with the electroencephalography (EEG)-recorded up-states that are commonly used for CLAS, it is the state of ventral frontal regions that is most critical for slow oscillation generation. Our findings advance models of how CLAS leads to enhancement of slow oscillations, sleep spindles and associated cognitive benefits and offer insight into how the effectiveness of brain stimulation techniques can be improved.

Sleep alterations as a function of 88 health indicators.

BACKGROUND: Alterations in sleep have been described in multiple health conditions and as a function of several medication effects. However, evidence generally stems from small univariate studies. Here, we apply a large-sample, data-driven approach to investigate patterns between in sleep macrostructure, quantitative sleep EEG, and health. METHODS: We use data from the MrOS Sleep Study, containing polysomnography and health data from a large sample (N = 3086) of elderly American men to establish associations between sleep macrostructure, the spectral composition of the electroencephalogram, 38 medical disorders, 2 health behaviors, and the use of 48 medications. RESULTS: Of sleep macrostructure variables, increased REM latency and reduced REM duration were the most common findings across health indicators, along with increased sleep latency and reduced sleep efficiency. We found that the majority of health indicators were not associated with objective EEG power spectral density (PSD) alterations. Associations with the rest were highly stereotypical, with two principal components accounting for 85-95% of the PSD-health association. PC1 consists of a decrease of slow and an increase of fast PSD components, mainly in NREM. This pattern was most strongly associated with depression/SSRI medication use and age-related disorders. PC2 consists of changes in mid-frequency activity. Increased mid-frequency activity was associated with benzodiazepine use, while decreases were associated with cardiovascular problems and associated medications, in line with a recently proposed hypothesis of immune-mediated circadian demodulation in these disorders. Specific increases in sleep spindle frequency activity were associated with taking benzodiazepines and zolpidem. Sensitivity analyses supported the presence of both disorder and medication effects. CONCLUSIONS: Sleep alterations are present in various health conditions.

Alpha anteriorization and theta posteriorization during deep sleep.

Brain states (wake, sleep, general anesthesia, etc.) are profoundly associated with the spatiotemporal dynamics of brain oscillations. Previous studies showed that the EEG alpha power shifted from the occipital cortex to the frontal cortex (alpha anteriorization) after being induced into a state of general anesthesia via propofol. The sleep research literature suggests that slow waves and sleep spindles are generated locally and propagated gradually to different brain regions. Since sleep and general anesthesia are conceptualized under the same framework of consciousness, the present study examines whether alpha anteriorization similarly occurs during sleep and how the EEG power in other frequency bands changes during different sleep stages. The results from the analysis of three polysomnography datasets of 234 participants show consistent alpha anteriorization during the sleep stages N2 and N3, beta anteriorization during stage REM, and theta posteriorization during stages N2 and N3. Although it is known that the neural circuits responsible for sleep are not exactly the same for general anesthesia, the findings of alpha anteriorization in this study suggest that, at macro level, the circuits for alpha oscillations are organized in the similar cortical areas. The spatial shifts of EEG power in different frequency bands during sleep may offer meaningful neurophysiological markers for the level of consciousness.

High prevalence of severe sleep cycle disruption in de novo acromegaly and underdiagnosis by common clinical screening tools: A prospective, observational, cross-sectional study.

CONTEXT: Although sleep disordered breathing (SDB) is well-recognised in acromegaly, most studies have reported heterogeneous, often heavily treated, groups and few have performed detailed sleep phenotyping at presentation. OBJECTIVE: To study SDB using the gold standard of polysomnography, in the largest group of newly-diagnosed, treatment-naïve patients with acromegaly. SETTING AND PATIENTS: 40 patients [22 males, 18 females; mean age 54 years (range 23-78)], were studied to: (i) establish the prevalence and severity of SDB (ii) assess the reliability of commonly employed screening tools [Epworth Sleepiness Scale (ESS) and overnight oxygen desaturation index (DI)] to detect SDB (iii) determine the extent to which sleep architecture is disrupted. RESULTS: Obstructive sleep apnoea (OSA), defined by the apnoea-hypopnoea index (AHI), was present in 79% of subjects (mild, n = 12; moderate, n = 5; severe, n = 14). However, in these individuals with OSA by AHI criteria, ESS (positive in 35% [n = 11]) and DI (positive in 71%: mild, n = 11; moderate, n = 6; severe, n = 5) markedly underestimated its prevalence/extent. Seventy-eight percent of patients exhibited increased arousal, with marked disruption of the sleep cycle, despite most (82%) having normal total time asleep. Fourteen patients spent longer in stage 1 sleep. Deeper sleep stages were severely attenuated in many subjects (reduced stage 2, n = 18; reduced slow wave sleep, n = 24; reduced rapid eye movement sleep, n = 32). CONCLUSION: Our study provides strong support for clinical guidelines that recommend screening for sleep apnoea syndrome in patients with newly-diagnosed acromegaly. Importantly, however, it highlights shortcomings in commonly recommended screening tools (questionnaires, desaturation index) and demonstrates the added value of polysomnography to allow timely detection of obstructive sleep apnoea and associated sleep cycle disruption.

Tripolar concentric ring electrodes for capturing localised electroencephalography signals during sleep.

By design, tripolar concentric ring electrodes (TCRE) provide more focal brain activity signals than conventional electroencephalography (EEG) electrodes placed further apart. This study compared spectral characteristics and rates of data loss to noisy epochs with TCRE versus conventional EEG signals recorded during sleep. A total of 20 healthy sleepers (12 females; mean [standard deviation] age 27.8 [9.6] years) underwent a 9-h sleep study. Participants were set up for polysomnography recording with TCRE to assess brain activity from 18 sites and conventional electrodes for EEG, eyes, and muscle movement. A fast Fourier transform using multitaper-based estimation was applied in 5-s epochs to scored sleep. Odds ratios with Bonferroni-adjusted 95% confidence intervals were calculated to determine the proportional differences in the number of noisy epochs between electrode types. Relative power was compared in frequency bands throughout sleep. Linear mixed models showed significant main effects of signal type (p < 0.001) and sleep stage (p < 0.001) on relative spectral power in each power band, with lower relative spectral power across all stages in TCRE versus EEG in alpha, beta, sigma, and theta activity, and greater delta power in all stages. Scalp topography plots showed distinct beta activation in the right parietal lobe with TCRE versus EEG. EEG showed higher rates of noisy epochs compared to TCRE (1.3% versus 0.8%, p < 0.001). TCRE signals showed marked differences in brain activity compared to EEG, consistent with more focal measurements and region-specific differences during sleep. TCRE may be useful for evaluating regional differences in brain activity with reduced muscle artefact compared to conventional EEG.

An acute bout of high-intensity exercise affects nocturnal sleep and sleep-dependent memory consolidation.

Acute exercise has been shown to affect long-term memory and sleep. However, it is unclear whether exercise-induced changes in sleep architecture are associated with enhanced memory. Recently, it has been shown that exercise followed by a nap improved declarative memory. Whether these effects transfer to night sleep and other memory domains has not yet been studied. Here, we investigate the influence of exercise on nocturnal sleep architecture and associations with sleep-dependent procedural and declarative memory consolidation. Nineteen subjects (23.68 ± 3.97 years) were tested in a balanced cross-over design. In two evening sessions, participants either exercised (high-intensity interval training) or rested immediately after encoding two memory tasks: (1) a finger tapping task and (2) a paired-associate learning task. Subsequent nocturnal sleep was recorded by polysomnography. Retrieval was conducted the following morning. High-intensity interval training lead to an increased declarative memory retention (p = 0.047, d = 0.40) along with a decrease in REM sleep (p = 0.012, d = 0.75). Neither procedural memory nor NREM sleep were significantly affected. Exercise-induced changes in N2 showed a positive correlation with procedural memory retention which did not withstand multiple comparison correction. Exploratory analyses on sleep spindles and slow wave activity did not reveal significant effects. The present findings suggest an exercise-induced enhancement of declarative memory which aligns with changes in nocturnal sleep architecture. This gives additional support for the idea of a potential link between exercise-induced sleep modifications and memory formation which requires further investigation in larger scaled studies.

A comparison of nocturnal upper trapezius muscle activity between chronic neck pain patients with sleep disturbance and healthy participants.

BACKGROUND: Sleep disturbances frequently occur in patients with chronic neck pain. In these patients, upper trapezius muscle dysfunction is observed during sleep. This study aimed to evaluate the trapezius muscle activity during sleep among patients with chronic neck pain and sleep disturbances for comparison with healthy subjects.  STUDY DESIGN: Cross-sectional study. METHODS: Patients with chronic neck pain and healthy subjects participated in the study. Two overnight polysomnography recordings were conducted for each subject. Surface electromyography was utilized to record the nocturnal activity of the right and left upper trapezius muscles throughout the night. The nocturnal upper trapezius activity recording was divided into the following parts: wakefulness, rapid eye movement sleep (REM), and non-rapid eye movement sleep (NREM). The nocturnal activity during NREM sleep was further divided into three parts (stage I NREM sleep, stage II NREM, and stage III NREM. Normalization of EMG signals was performed. The normalized value of nocturnal activity was derived for analysis. RESULTS: Among 15 patients with chronic neck pain and 15 healthy subjects, statistically significant differences were observed in the nocturnal activity of the upper trapezius. Compared to healthy subjects, the nocturnal activity of the upper trapezius was significantly higher during wakefulness, REM sleep, and NREM II and III sleep in patients with chronic neck pain and sleep disturbances. CONCLUSION: There was higher nocturnal upper trapezius activity in patients with chronic neck pain compared to healthy controls. The findings suggest a possible pathophysiological mechanism that may relate to chronic neck pain. TRIAL REGISTRATION: CTRI/2019/09/021028.

The clinical characteristic of catathrenia: a new look at an old issue-a systematic review of existing literature.

STUDY OBJECTIVES: The International Classification of Sleep Disorders categorized catathrenia as a respiratory disorder, but there are doubts whether episodes appear during rapid eye movement (REM) sleep or the non-rapid eye movement (NREM), their duration, and symptoms. The main objectives were to identify the most common features and relations of catathrenia. METHODS: PubMed, Embase, and Web of Science were searched according to the PRISMA 2020 guidelines. The Joanna Briggs Institute and the ROBINS-I tools were chosen to assess the risk of bias. RESULTS: A total of 288 records were identified, 31 articles were included. The majority of the studies had a moderate risk of bias. 49.57% of episodes occurred during the NREM sleep, while 46% took place during REM. In 60.34% females, catathrenia was more common in the NREM, while in 59.26% of males was in REM sleep (p < 0.05). Females and obese individuals were found to have shorter episodes (p < 0.05). Age was inversely correlated with minimal episodes duration (r = - 0.34). The continuous positive airway pressure (CPAP) therapy was inversely correlated with the maximal episode duration (r = - 0.48). CONCLUSIONS: Catathrenia occurs with similar frequency in both genders. The most frequent symptoms embraced groaning, awareness of disturbing bedpartners, and daytime somnolence-not confirmed by the Epworth Sleepiness Scale. The episodes occur more frequently in NREM than in REM sleep. Catathrenia may be considered as a sex-specific condition. The effects of CPAP treatment leading to shortening episodes duration, which may indicate the respiratory origin of catathrenia.

Performance Evaluation of a New Sport Watch in Sleep Tracking: A Comparison against Overnight Polysomnography in Young Adults.

Introduction: This study aimed to validate the ability of a prototype sport watch (Polar Electro Oy, FI) to recognize wake and sleep states in two trials with and without an interval training session (IT) 6 h prior to bedtime. Methods: Thirty-six participants completed this study. Participants performed a maximal aerobic test and three polysomnography (PSG) assessments. The first night served as a device familiarization night and to screen for sleep apnea. The second and third in-home PSG assessments were counterbalanced with/without IT. Accuracy and agreement in detecting sleep stages were calculated between PSG and the prototype. Results: Accuracy for the different sleep stages (REM, N1 and N2, N3, and awake) as a true positive for the nights without exercise was 84 ± 5%, 64 ± 6%, 81 ± 6%, and 91 ± 6%, respectively, and for the nights with exercise was 83 ± 7%, 63 ± 8%, 80 ± 7%, and 92 ± 6%, respectively. The agreement for the sleep night without exercise was 60.1 ± 8.1%, k = 0.39 ± 0.1, and with exercise was 59.2 ± 9.8%, k = 0.36 ± 0.1. No significant differences were observed between nights or between the sexes. Conclusion: The prototype showed better or similar accuracy and agreement to wrist-worn consumer products on the market for the detection of sleep stages with healthy adults. However, further investigations will need to be conducted with other populations.

Sleep-related bruxism, microarousals and oxyhaemoglobin desaturations in sleep stages: A cross-sectional study in a large apnoeic population.

BACKGROUND: Sleep-related bruxism (SB) is the habit of grinding or clenching the teeth during sleep, mediated by the non-peripheral central nervous system. PURPOSE: The objectives of this cross-sectional study were to evaluate associations between SB, microarousals and oxyhaemoglobin desaturations and to compare the frequency of SB and microarousals in sleep stages, in an apnoeic population. METHODS: Two hundred and forty individuals composed the sample, who underwent a single full-night polysomnography. Self-reports and clinical inspections were not considered for assessing SB. The polysomnographic assessment of SB was performed using electrodes placed on masseter muscles and chin. SB was defined as more than two events of rhythmic masticatory muscle activity per hour of sleep. Microarousals were considered when there were abrupt changes in electroencephalogram frequencies, without complete awakening, lasting from 3 to 15 s. Oxyhaemoglobin desaturations were defined as significant drops (≥3%) in basal oxygen saturations. With these data, SB, microarousals and oxyhaemoglobin desaturations were evaluated and submitted to statistical analysis. RESULTS: Statistically significant differences were observed between bruxers and non-bruxers when comparing the rates of microarousals (p < .001) and oxyhaemoglobin desaturations (p = .038). There was a higher number of SB and microarousals in NREM (non-rapid eye movement) two sleep stage (p < 0.001). Bruxers had a greater risk of higher numbers of microarousals (OR = 1.023; p = .003), which did not occur for oxyhaemoglobin desaturations (OR = 0.998; p = .741). CONCLUSIONS: A higher number of microarousals presents relationship with SB; associations between SB and oxyhaemoglobin desaturations remained inconclusive; higher frequency of SB and microarousals was observed in NREM 2 sleep stage.

Simulated quick returns in a laboratory context and effects on sleep and pre-sleep arousal between shifts: a crossover controlled trial.

This trial presents a laboratory model investigating the effect of quick returns (QRs, <11 h time off between shifts) on sleep and pre-sleep arousal. Using a crossover design, 63 participants worked a simulated QR condition (8 h time off between consecutive evening- and day shifts) and a day-day (DD) condition (16 h time off between consecutive day shifts). Participants slept at home and sleep was measured using a sleep diary and sleep radar. Compared to the DD condition, the QR condition reduced subjective and objective total sleep time by approximately one hour (both p < .001), reduced time in light- (p < .001), deep- (p = .004), rapid eye movement (REM, p < .001), percentage of REM sleep (p = .023), and subjective sleep quality (p < .001). Remaining sleep parameters and subjective pre-sleep arousal showed no differences between conditions. Results corroborate previous field studies, validating the QR model and indicating causal effects of short rest between shifts on common sleep parameters and sleep architecture.

This trial proposes a laboratory model to investigate the consequences of quick returns (QRs, <11h time off between shifts) on subjective/objective sleep and pre-sleep arousal. QRs reduced total sleep time, light-, deep-, REM sleep, whereas pre-sleep arousal was unaffected. Results emphasise the importance of ensuring sufficient rest time between shifts.Abbreviation: QR: Quick return; DD: Day-day; NREM: Non-rapid eye movement; REM: Rapid eye movement; PSG: Polysomnography; TIB: Time in bed; SOL: Sleep onset latency; WASO: Wake after sleep onset; TST: Total sleep time; EMA: Early morning awakening; PSAS: Pre-Sleep Arousal Scale; MEQ: Morning-Evening Questionnaire; LMM: Linear mixed model; EMM: Estimated marginal mean; SD: Standard deviation; SE: Standard error; d: Cohens' d; h: hours; m: minutes.

Analysing the impact of body position shift on sleep architecture and stage transition: A comprehensive multidimensional study using event-synchronised polysomnography data.

Although understanding the physiological mechanisms of obstructive sleep apnea (OSA) is important for treating OSA, limited studies have examined OSA patients' sleep architecture at the epoch-by-epoch level and analysed the impact of sleep position and stage on OSA pathogenesis. The epoch-labelled polysomnogram was analysed multidimensionally to investigate the effect of sleep position on the sleep architecture and risk factors of apnea in patients with OSA. This retrospective multicentric case-control study reviewed full-night diagnostic polysomnography of 6983 participants. The difference in the proportion of time spent supine during non-rapid eye movement (NREM) and REM stages, and the mean duration of respiratory events per body position were evaluated. The frequency of sleep stage transition per body position shift type was computed. Further subgroup analysis was performed based on OSA severity and positional dependency. Supine time in patients with OSA varied across sleep stages, with lower proportions in N3 and REM, and shorter durations with severity. Patients with OSA spent less time in supine positions during N3 and REM, and experienced longer apnea events in both positions compared to the control group. The frequency of all sleep stage transitions increased with OSA severity and was higher among non-positional OSA than positional OSA and the control group, regardless of body position shift type. The sleep stage transition from N3 and REM to wakefulness was notably heightened during position shift. Understanding the sleep architecture of patients with OSA requires analysing various sleep characteristics including sleep position simultaneously, with future studies focusing on position detection to predict sleep stages and respiratory events.

Differences in intestinal motility during different sleep stages based on long-term bowel sounds.

BACKGROUND AND OBJECTIVES: This study focused on changes in intestinal motility during different sleep stages based on long-term bowel sounds. METHODS: A modified higher order statistics algorithm was devised to identify the effective bowel sound segments. Next, characteristic values (CVs) were extracted from each bowel sound segment, which included 4 time-domain, 4 frequency-domain and 2 nonlinear CVs. The statistical analysis of these CVs corresponding to the different sleep stages could be used to evaluate the changes in intestinal motility during sleep. RESULTS: A total of 6865.81 min of data were recorded from 14 participants, including both polysomnographic data and bowel sound data which were recorded simultaneously from each participant. The average accuracy, sensitivity and specificity of the modified higher order statistics detector were 96.46 ± 2.60%, 97.24 ± 2.99% and 94.13 ± 4.37%. In addition, 217088 segments of effective bowel sound corresponding to different sleep stages were identified using the modified detector. Most of the CVs were statistically different during different sleep stages ([Formula: see text]). Furthermore, the bowel sounds were low in frequency based on frequency-domain CVs, high in energy based on time-domain CVs and low in complexity base on nonlinear CVs during deep sleep, which was consistent with the state of the EEG signals during deep sleep. CONCLUSIONS: The intestinal motility varies by different sleep stages based on long-term bowel sounds using the modified higher order statistics detector. The study indicates that the long-term bowel sounds can well reflect intestinal motility during sleep. This study also demonstrates that it is technically feasible to simultaneously record intestinal motility and sleep state throughout the night. This offers great potential for future studies investigating intestinal motility during sleep and related clinical applications.

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.

BACKGROUND: The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via deep learning is emerging as a decent alternative for their convenience and potential accuracy. However, sound-based sleep staging has only been studied using in-laboratory sound data. In real-world sleep environments (homes), there is abundant background noise, in contrast to quiet, controlled environments such as laboratories. The use of sound-based sleep staging at homes has not been investigated while it is essential for practical use on a daily basis. Challenges are the lack of and the expected huge expense of acquiring a sufficient size of home data annotated with sleep stages to train a large-scale neural network. OBJECTIVE: This study aims to develop and validate a deep learning method to perform sound-based sleep staging using audio recordings achieved from various uncontrolled home environments. METHODS: To overcome the limitation of lacking home data with known sleep stages, we adopted advanced training techniques and combined home data with hospital data. The training of the model consisted of 3 components: (1) the original supervised learning using 812 pairs of hospital polysomnography (PSG) and audio recordings, and the 2 newly adopted components; (2) transfer learning from hospital to home sounds by adding 829 smartphone audio recordings at home; and (3) consistency training using augmented hospital sound data. Augmented data were created by adding 8255 home noise data to hospital audio recordings. Besides, an independent test set was built by collecting 45 pairs of overnight PSG and smartphone audio recording at homes to examine the performance of the trained model. RESULTS: The accuracy of the model was 76.2% (63.4% for wake, 64.9% for rapid-eye movement [REM], and 83.6% for non-REM) for our test set. The macro F1-score and mean per-class sensitivity were 0.714 and 0.706, respectively. The performance was robust across demographic groups such as age, gender, BMI, or sleep apnea severity (accuracy 73.4%-79.4%). In the ablation study, we evaluated the contribution of each component. While the supervised learning alone achieved accuracy of 69.2% on home sound data, adding consistency training to the supervised learning helped increase the accuracy to a larger degree (+4.3%) than adding transfer learning (+0.1%). The best performance was shown when both transfer learning and consistency training were adopted (+7.0%). CONCLUSIONS: This study shows that sound-based sleep staging is feasible for home use. By adopting 2 advanced techniques (transfer learning and consistency training) the deep learning model robustly predicts sleep stages using sounds recorded at various uncontrolled home environments, without using any special equipment but smartphones only.

Automated sleep staging in people with intellectual disabilities using heart rate and respiration variability.

BACKGROUND: People with intellectual disabilities (ID) have a higher risk of sleep disorders. Polysomnography (PSG) remains the diagnostic gold standard in sleep medicine. However, PSG in people with ID can be challenging, as sensors can be burdensome and have a negative influence on sleep. Alternative methods of assessing sleep have been proposed that could potentially transfer to less obtrusive monitoring devices. The goal of this study was to investigate whether analysis of heart rate variability and respiration variability is suitable for the automatic scoring of sleep stages in sleep-disordered people with ID. METHODS: Manually scored sleep stages in PSGs of 73 people with ID (borderline to profound) were compared with the scoring of sleep stages by the CardioRespiratory Sleep Staging (CReSS) algorithm. CReSS uses cardiac and/or respiratory input to score the different sleep stages. Performance of the algorithm was analysed using input from electrocardiogram (ECG), respiratory effort and a combination of both. Agreement was determined by means of epoch-per-epoch Cohen's kappa coefficient. The influence of demographics, comorbidities and potential manual scoring difficulties (based on comments in the PSG report) was explored. RESULTS: The use of CReSS with combination of both ECG and respiratory effort provided the best agreement in scoring sleep and wake when compared with manually scored PSG (PSG versus ECG = kappa 0.56, PSG versus respiratory effort = kappa 0.53 and PSG versus both = kappa 0.62). Presence of epilepsy or difficulties in manually scoring sleep stages negatively influenced agreement significantly, but nevertheless, performance remained acceptable. In people with ID without epilepsy, the average kappa approximated that of the general population with sleep disorders. CONCLUSIONS: Using analysis of heart rate and respiration variability, sleep stages can be estimated in people with ID. This could in the future lead to less obtrusive measurements of sleep using, for example, wearables, more suitable to this population.

Surgical treatment for obstructive sleep apnea: effect on sleep architecture.

PURPOSE: Investigate the effect of surgical treatment of obstructive sleep apnea syndrome (OSA) on sleep architecture. METHODS: Observational retrospective analysis of polysomnographic data of adults diagnosed with OSA, submitted to surgical treatment. Median (25-75th percentile) was used to present the data. RESULTS: Data were available for 76 adults, 55 men and 21 women, with median age of 49.0 years (41.0-62.0), body mass index of 27.3 kg/m2 (25.3-29.3) and AHI of 17.4 per hour (11.3-22.9) before surgeries. Preoperatively, 93.4% of patients had an abnormal distribution of at least one of the sleep phases. After surgical treatment, we found a significant increase in median N3 sleep percent from 16.9% (8.3-22-7) to 18.9% (15.5-25.4) (p = 0.003). Postoperatively, 18.6% patients that had an abnormal preoperative N1 sleep phase distribution had a normalization of this sleep phase, as also occurred to N2, N3 and REM sleep phases in 44.0%, 23.3% and 63.6% of patients, respectively. CONCLUSION: This study aims to show the impact of OSA treatment, not only on respiratory events but also on other polysomnographic data often underestimated. Upper airway surgeries have shown to be effective in sleep architecture improvements. There is a trend for sleep distribution normalization, with increase of time spend in profound sleep.

Evaluation of the Accuracy of Contactless Consumer Sleep-Tracking Devices Application in Human Experiment: A Systematic Review and Meta-Analysis.

Compared with the gold standard, polysomnography (PSG), and silver standard, actigraphy, contactless consumer sleep-tracking devices (CCSTDs) are more advantageous for implementing large-sample and long-period experiments in the field and out of the laboratory due to their low price, convenience, and unobtrusiveness. This review aimed to examine the effectiveness of CCSTDs application in human experiments. A systematic review and meta-analysis (PRISMA) of their performance in monitoring sleep parameters were conducted (PROSPERO: CRD42022342378). PubMed, EMBASE, Cochrane CENTRALE, and Web of Science were searched, and 26 articles were qualified for systematic review, of which 22 provided quantitative data for meta-analysis. The findings show that CCSTDs had a better accuracy in the experimental group of healthy participants who wore mattress-based devices with piezoelectric sensors. CCSTDs' performance in distinguishing waking from sleeping epochs is as good as that of actigraphy. Moreover, CCSTDs provide data on sleep stages that are not available when actigraphy is used. Therefore, CCSTDs could be an effective alternative tool to PSG and actigraphy in human experiments.

A novel automated robust dual-channel EEG-based sleep scoring system using optimal half-band pair linear-phase biorthogonal wavelet filter bank.

Nowadays, the hectic work life of people has led to sleep deprivation. This may further result in sleep-related disorders and adverse physiological conditions. Therefore, sleep study has become an active research area. Sleep scoring is crucial for detecting sleep-related disorders like sleep apnea, insomnia, narcolepsy, periodic leg movement (PLM), and restless leg syndrome (RLS). Sleep is conventionally monitored in a sleep laboratory using polysomnography (PSG) which is the recording of various physiological signals. The traditional sleep stage scoring (SSG) done by professional sleep scorers is a tedious, strenuous, and time-consuming process as it is manual. Hence, developing a machine-learning model for automatic SSG is essential. In this study, we propose an automated SSG approach based on the biorthogonal wavelet filter bank's (BWFB) novel least squares (LS) design. We have utilized a huge Wisconsin sleep cohort (WSC) database in this study. The proposed study is a pioneering work on automatic sleep stage classification using the WSC database, which includes good sleepers and patients suffering from various sleep-related disorders, including apnea, insomnia, hypertension, diabetes, and asthma. To investigate the generalization of the proposed system, we evaluated the proposed model with the following publicly available databases: cyclic alternating pattern (CAP), sleep EDF, ISRUC, MIT-BIH, and the sleep apnea database from St. Vincent's University. This study uses only two unipolar EEG channels, namely O1-M2 and C3-M2, for the scoring. The Hjorth parameters (HP) are extracted from the wavelet subbands (SBS) that are obtained from the optimal BWFB. To classify sleep stages, the HP features are fed to several supervised machine learning classifiers. 12 different datasets have been created to develop a robust model. A total of 12 classification tasks (CT) have been conducted employing various classification algorithms. Our developed model achieved the best accuracy of 83.2% and Cohen's Kappa of 0.7345 to reliably distinguish five sleep stages, using an ensemble bagged tree classifier with 10-fold cross-validation using WSC data. We also observed that our system is either better or competitive with existing state-of-art systems when we tested with the above-mentioned five databases other than WSC. This method yielded promising results using only two EEG channels using a huge WSC database. Our approach is simple and hence, the developed model can be installed in home-based clinical systems and wearable devices for sleep scoring.

Diagnostic Value of the Bispectral Index to Assess Sleep Quality after Elective Surgery in Intensive Care Unit.

Purpose: Monitoring and improving sleep quality may help recovery from major illness. Polysomnography is a gold standard for measuring sleep quality, but routine use is not practical. The goal of this study is to investigate the diagnostic accuracy of an alternative monitor, the Bispectral Index (BIS), for evaluating the quality of sleep-in postoperative patients in the intensive care unit (ICU). Study design: An observational study. Materials and methods: Patients admitted to postoperative ICU after elective major noncardiac surgery were monitored with both BIS and PSG during the first night. The temporally synchronized data from both monitors were obtained for measurement of the association. Clinical outcomes were compared between patients with different postoperative sleep quality. Results: Thirty-three patients were enrolled in this study. For determining the average BIS index associated with good postoperative sleep quality, receiver operating characteristics (ROC) curve was generated. Area under the ROC curve (AUC) was 0.65. The cutoff with best discriminability was 75 with a sensitivity of 68% and a specificity of 56%. Compared with those with good and poor postoperative sleep quality, there were no differences in main postoperative outcomes including duration of mechanical ventilation and ICU stay. Although the quality of sleep after surgery of all subjects with postoperative delirium was poor, the incidence of delirium between the groups did not significantly differ (0% vs 10.3%; p = 0.184). Conclusion: The monitoring of BIS is a viable tool for evaluating sleep quality in mechanically ventilated patients in the postoperative ICU with acceptable precision. Trial registration: www.clinicaltrials.in.th, TCTR20200310005. How to cite this article: Sirilaksanamanon P, Thawitsri T, Charuluxananan S, Chirakalwasan N. Diagnostic Value of the Bispectral Index to Assess Sleep Quality after Elective Surgery in Intensive Care Unit. Indian J Crit Care Med 2023;27(11):795-800.