Journal articles: 'Sleep. Sleep movements. Monitoring, Physiologic' – Grafiati (2024)

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Polysomnography (PSG) is the gold standard diagnostic test for several sleep disorders. It records , analyzes, & interprets multiple simultaneous physiologic characteristics during sleep. These parameters include brain waves, the oxygen level in the blood, heart rate and breathing, body position, as well as eye and leg movements, along with synchronized audiovisual monitoring. Moreover, in certain conditions, additional parameters may be included such as esophageal pH monitoring, esophageal manometry , and overnight blood pressure monitoring. The test is usually performed at a sleep disorders unit within a hospital or at a sleep disorders centre. Sleep architecture is largely divided into non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. NREM is further divided into three stages: N1, N2, and N3 : N3 being the deepest stage of sleep. REM sleep alternates with NREM sleep and a normal person usually has 4–6 cycles of REM and NREM sleep . Monitoring of the different sleep stages, sleep interruptions, movements, and the other respiratory and cardiac signals are clinically helpful for identifying the nature of patient’s sleep problems and assessing response to treatment. JMS 2015;18(2):165-166

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Magneto-elastic bilayers (BLs), consisting of a magnetostrictive layer and a non-magnetic counter layer, show highest sensitivity with respect to bending. This paper describes a biomedical application in the field of sleep apnea screening. A multi-parametric detector fixed at the thorax contains two BLs. One BL yields a skin curvature sensor adjusting itself to curvature variations given by physiological activities. The second BL exhibits a free end thus working as a motion sensor. The two signals are fed into artificial neural networks for the detection of events like normal respiration and apneas, as well as body movements and position.

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Abstract Introduction Sleep bruxism (BXM) is the result of rhythmic muscular masticatory activity (RMMA) and can be captured by masseters surface electromyography (sEMG). Despite the multiple adverse negative consequences of BXM, a simple reliable home diagnostic device is currently unavailable, with in laboratory audio-video polysomnography (type I PSG) remaining the gold standard diagnostic tool. Mandibular movements (MM) recordings during sleep can readily identify RMMA, are simple to set up and can be easily repeated from night to night. Here, we aimed to identify stereotypical MM in patients with BXM, and to develop RMMA automatic detection and BXM diagnosis using an artificial intelligence-based approach. Methods MM were recorded by a dedicated sensor (Sunrise, Namur, Belgium) in 12 patients with BXM during type I PSG. The Sunrise system consists of a coin-sized hardware that is comfortably placed on the subject’s chin. Its embedded inertial measurement unit communicates via Bluetooth with a smartphone and automatically transfers MM signals to a cloud-based infrastructure at the end of the night. Data processing and analysis are then performed in Python programming language. A time series cluster analysis was applied to sequences of masseters sEMG and MM signals during BXM episodes (n=300) and during spontaneous micro-arousals (n=300). Then, a convolutional neuronal network (CNN) was developed to identify BXM and distinguish it from spontaneous micro-arousals while exclusively relying on MM signal. Results Based on the cluster analysis, BXM periods were characterized by a specific pattern of MM signals (higher frequency and amplitude), which was closely associated with the sEMG signals but clearly differed from the MM signal patterns during micro-arousals. CNN-based classifier distinguished the BXM events from other RMMAs during micro-arousals and respiratory efforts with an overall accuracy of 91%. Conclusion Sleep bruxism can be automatically identified, quantified, and characterized with mandibular movements analysis supported by artificial intelligence technology. Support This work was supported by the French National Research Agency (ANR-12-TECS-0010), in the framework of the “Investissem*nts d’avenir” program (ANR-15-IDEX-02). https://life.univ-grenoble-alpes.fr.

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Abstract Introduction Sleep is a fundamental necessity for health and is commonly disrupted in the perioperative period. Technological improvements leveraging dry electroencephalographic (EEG) sensors have opened the door for large-scale quantitative assessments of sleep in relation to perioperative outcomes. Methods Patients utilized the Dreem (Rhythm, New York USA), a wireless EEG headband, to acquire their own preoperative nocturnal sleep records at home. Following cardiac surgery, postoperative recordings were obtained with staff assistance until postoperative night 7. Sleep records were scored as rapid eye movement (REM) and non-rapid eye movement (NREM) stages N1-N3, using modified American Academy of Sleep Medicine guidelines. Results Of 100 patients enrolled for perioperative sleep recordings, 74 patients provided 132 preoperative records; 80% were scorable with a median total sleep time (TST) of 209.8 minutes. TST was distributed as 8.3% N1, 70.6% N2, 2.1% N3 and 19% REM, consistent with expected sleep structure in geriatric populations. EEG markers for staging sleep were evaluated in the scorable records: 92% with sleep spindles, 98% with K-complexes, 69% with slow waves, 92% with sawtooth waves, and 80% with rapid eye movements. Among 26 patients with multiple preoperative sleep recordings, no significant within-subject differences in sleep structure were observed (all p > 0.05, paired Wilcoxon sign-rank test). 270 postoperative nocturnal sleep recordings were obtained from 83 patients, 70% of which were scorable. TST in scorable postoperative records was distributed as 14.9% N1, 78.6% N2, 0.9% N3 and 5.6% REM. Durations of REM and N3 sleep were significantly reduced in postoperative (POD 1-4) overnight recordings compared to preoperative measurements (Skillings–Mack test, p < 0.001 and p = 0.02 for REM and N3, respectively). Conclusion Wireless EEG devices enhance the feasibility of assaying perioperative sleep. A single night of unattended, ambulatory sleep monitoring is sufficient to establish a preoperative baseline. Multiple preoperative and postoperative sleep studies were tolerated by patients, which showed reductions of N3 and REM sleep in the early postoperative period. This study demonstrates the feasibility of using the Dreem for monitoring sleep macro- and microstructural EEG elements in the perioperative setting. Support (if any):

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Abstract Objectives Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the complexity of PSG signals and physiological heterogeneity between patients. Deep neural networks, which have recently achieved expert-level performance for other complex medical tasks, are ideally suited to PSG scoring, given sufficient training data. Methods We used a combination of deep recurrent and convolutional neural networks (RCNN) for supervised learning of clinical labels designating sleep stages, sleep apnea events, and limb movements. The data for testing and training were derived from 10 000 clinical PSGs and 5804 research PSGs. Results When trained on the clinical dataset, the RCNN reproduces PSG diagnostic scoring for sleep staging, sleep apnea, and limb movements with accuracies of 87.6%, 88.2% and 84.7% on held-out test data, a level of performance comparable to human experts. The RCNN model performs equally well when tested on the independent research PSG database. Only small reductions in accuracy were noted when training on limited channels to mimic at-home monitoring devices: frontal leads only for sleep staging, and thoracic belt signals only for the apnea-hypopnea index. Conclusions By creating accurate deep learning models for sleep scoring, our work opens the path toward broader and more timely access to sleep diagnostics. Accurate scoring automation can improve the utility and efficiency of in-lab and at-home approaches to sleep diagnostics, potentially extending the reach of sleep expertise beyond specialty clinics.

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Abstract Introduction Sleep associated seizures especially Nocturnal Frontal Lobe Epilepsy (NFLE) represents a spectrum of challenging clinical manifestations presenting as complex nocturnal movements/behaviors, making the diagnosis often difficult. Report of Case A 64 y/o male, with history of ongoing complex movements occurring during his sleep, with no history of strokes or neurological deficits. Had extensive neurologic workup (all negative) including routine electroencephalogram (EEG), prolonged inpatient EEG (12 hours), and MRI of the brain. Home sleep study showing moderate obstructive sleep apnea (OSA) AHI 24/hour successfully treated with CPAP therapy (residual AHI 1.7/hour) with improved nighttime symptoms initially. Wife recalls events as happening only at night while sleep, as patient often confused upon waking up in the morning, at times appear to sit up and smack his lips. No nighttime hallucinations, sleep paralysis, or acting out dreams were reported. Had two episodes associated with tongue biting and loss of bladder control. Another episode happened after a daytime nap, patient went outside and was mowing his lawn, went “completely blank “, appeared confused. No daytime or nighttime seizures were ever noticed. Patient do not recall any of the above events. Repeat EEG was normal. MRI/MRA of the head /neck showed small tiny focus in left frontoparietal lobe, suggesting remote cortical ischemic injury. Polysomnography (PSG) with seizure montage showed Interictal epileptic discharges (IEDs) foci recorded in the frontal/frontopolar leads without accompanying body movements. Interictal spike and wave activity seen during stage N2. Initially treated with carbamazepine (had skin reaction) switched to levetiracetam with complete resolution of his symptoms. Conclusion This case illustrates the importance of reviewing the clinical history, behavior semiology, and diagnostic ancillary testing such as polysomnography with EEG monitoring in distinguishing nocturnal epileptic seizures from other nocturnal complex behavior disorders and parasomnias.

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Background The Pfizer-BioNTech COVID-19 vaccine uses a novel messenger RNA technology to elicit a protective immune response. Short-term physiologic responses to the vaccine have not been studied using wearable devices. Objective We aim to characterize physiologic changes in response to COVID-19 vaccination in a small cohort of participants using a wearable device (WHOOP Strap 3.0). This is a proof of concept for using consumer-grade wearable devices to monitor response to COVID-19 vaccines. Methods In this prospective observational study, physiologic data from 19 internal medicine residents at a single institution that received both doses of the Pfizer-BioNTech COVID-19 vaccine was collected using the WHOOP Strap 3.0. The primary outcomes were percent change from baseline in heart rate variability (HRV), resting heart rate (RHR), and respiratory rate (RR). Secondary outcomes were percent change from baseline in total, rapid eye movement, and deep sleep. Exploratory outcomes included local and systemic reactogenicity following each dose and prophylactic analgesic use. Results In 19 individuals (mean age 28.8, SD 2.2 years; n=10, 53% female), HRV was decreased on day 1 following administration of the first vaccine dose (mean –13.44%, SD 13.62%) and second vaccine dose (mean –9.25%, SD 22.6%). RHR and RR showed no change from baseline after either vaccine dose. Sleep duration was increased up to 4 days post vaccination, after an initial decrease on day 1. Increased sleep duration prior to vaccination was associated with a greater change in HRV. Local and systemic reactogenicity was more severe after dose two. Conclusions This is the first observational study of the physiologic response to any of the novel COVID-19 vaccines as measured using wearable devices. Using this relatively small healthy cohort, we provide evidence that HRV decreases in response to both vaccine doses, with no significant changes in RHR or RR. Sleep duration initially decreased following each dose with a subsequent increase thereafter. Future studies with a larger sample size and comparison to other inflammatory and immune biomarkers such as antibody response will be needed to determine the true utility of this type of continuous wearable monitoring in regards to vaccine responses. Our data raises the possibility that increased sleep prior to vaccination may impact physiologic responses and may be a modifiable way to increase vaccine response. These results may inform future studies using wearables for monitoring vaccine responses. Trial Registration ClinicalTrials.gov NCT04304703; https://www.clinicaltrials.gov/ct2/show/NCT04304703

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<p>Sleep plays an important role as it helps human body to rejuvenate, boosts mental function and manage stress. Sleep is restorative function which enhances muscle growth, repairs tissues, maintains health and make physical appearance look or feel better. The lack of sleep in human body can increase the risk of diseases which are asthma, diabetes, depression. For healthy physiological function, sleep is essential and has strong relation to mental condition. Easy way of sleep management is considered for maintaining good mental health. Numerous scientists, doctors and researchers have proposed various ways to monitor sleep, some of those best tests are polysomnography test and actigraphy test. However, taking sleep test covering the whole body with wires and electrodes which is polysomnography test is uncomfortable for patients, and sensors used for different approaches like this are costly and often require overnight treatment and expert monitoring in clinics. Therefore, easy way of detecting roll-over movements which is convenient for patients to wear is proposed. Accelerometer ADXL335 sensor is taped on socks during sleep which is comfortable for patients to wear and do not cause any inconvenience during sleep. Algorithm is proposed to read the dataset and count the roll-over during the sleep based on threshold. Resulting the number of roll-over detected during a sleep period.</p>

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Abstract Introduction Sleep deprivation and circadian misalignment impairs human sensorimotor performance and reduces vigilant attention, which increases the potential for errors in occupations that require 24-hour operations. The psychom*otor vigilance task (PVT) is the gold-standard measure for evaluating the impact of sleepiness on performance, however, it is not practical to administer in many operational environments, because it only provides a snapshot of performance and requires an individual to focus on the task for several minutes, multiple times over a work shift. As a result, passive, continuous monitoring of sleepiness is desirable for operational environments. The goal of the present study was to determine if complex oculomotor behavioral metrics track PVT performance during sleep deprivation. Methods Twelve healthy adults (mean age 24.8 ± 5.4 years; 6F) maintained a fixed schedule with 8.5 hours in bed for two weeks, during which they abstained from caffeine, alcohol, and other medications, followed by a ~24 hours constant routine laboratory stay. Participants completed the PVT and a radial step-ramp ocular tracking task hourly throughout the study. Twelve oculometrics were derived from smooth pursuit and saccadic eye movements collected through video-oculography and were compared to the PVT and Karolinska Sleepiness Scale (KSS) using linear regression and receiver operating characteristic curves. Results Nine oculometrics spanning pursuit, saccade, and directional motion processing performance correlated with the PVT and KSS (p &lt; 0.05), including: (a) pursuit latency; (b) open-loop pursuit acceleration; (c) proportion smooth; (d) steady-state pursuit gain; (e) saccadic amplitude; (f) saccadic dispersion; (g) saccadic rate; (h) direction asymmetry; and (i) direction noise. Conclusion The oculometrics that we examined exhibited a distinct pattern that tracked PVT performance. Future studies should examine whether these metrics can be extracted through passive monitoring techniques. Support None

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Abstract Introduction The sleep onset period, involving so-called stage N1 sleep largely, is characterized by a reduction in the amount of alpha activity compared to wakefulness. Various kinds of physiological and psychological changes are also apparent, such as slow eye movements, changes in muscle tonus, and the hypnagogic dream-like mentation. These phenomena are thought to be the reflection of dynamic alterations in the brain during the transition period, however, details of these changes have still been uncovered. Methods We aimed to investigate a dynamic shift in the brain connectivity at sleep onset using the method of EEG-fMRI simultaneous recording. Twenty-three healthy subjects participated. EEG/fMRI were recorded simultaneously during an hour’s nap in a 3T-MRI scanner and real-time monitoring of EEG was performed. To record the transition period between multiple times, an experimenter inside a scanner room touched a subject’s foot for inducing arousal when a shift to NREM sleep stage 1 was observed. EEG data were scored according to the AASM criteria. Based on sleep stages defined by polysomnographic findings, we investigated alterations in functional connectivity of sleep- and wake- promoting regions within the hypothalamus and other areas including the thalamus. Results Posterior alpha power showed significant positive correlation with BOLD signals in the anterior and medial dorsal thalamus. Connectivity between the thalamus and cortical regions reduced sharply in the descent to sleep stage. Meanwhile, BOLD signals of the sleep- and wake- promoting regions within the hypothalamus fluctuated with certain temporal lags from fluctuations of alpha rhythm at sleep onset. Conclusion Present findings provide preliminary evidence of dynamics of wake- and sleep- promoting regions in the human brain in vivo. Our data also support the hypothesis that reduced thalamocortical connectivity which limits the capacity to integrate information is associated with the transition of consciousness at sleep onset. Support None

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An unobtrusive bed integrated system for monitoring physiological parameters during sleep is presented and evaluated. The system uses textile electrodes attached to a bed sheet for measuring multiple channels of electrocardiogram. The channels are also combined in order to form several additional ECG leads. One lead at a time is selected for beat-to-beat-interval detection. The system also includes force sensors located under a bed post for detecting respiration and movements. The movement information is also used to assist in heart rate detection and combining the ECG derived respiration information with respiration information derived from force sensors, is investigated. The authors tested the system with ten subjects in one hour recordings and achieved an average of 95.9% detection coverage and 99 percentile absolute error of 3.47 ms for the BB-interval signal. The relative mean absolute error of the detected respiration cycle lengths was 2.1%.

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PurposeA prospective cohort study for pregnant women, the Maternity Log study, was designed to construct a time-course high-resolution reference catalogue of bioinformatic data in pregnancy and explore the associations between genomic and environmental factors and the onset of pregnancy complications, such as hypertensive disorders of pregnancy, gestational diabetes mellitus and preterm labour, using continuous lifestyle monitoring combined with multiomics data on the genome, transcriptome, proteome, metabolome and microbiome.ParticipantsPregnant women were recruited at the timing of first routine antenatal visits at Tohoku University Hospital, Sendai, Japan, between September 2015 and November 2016. Of the eligible women who were invited, 65.4% agreed to participate, and a total of 302 women were enrolled. The inclusion criteria were age ≥20 years and the ability to access the internet using a smartphone in the Japanese language.Findings to dateStudy participants uploaded daily general health information including quality of sleep, condition of bowel movements and the presence of nausea, pain and uterine contractions. Participants also collected physiological data, such as body weight, blood pressure, heart rate and body temperature, using multiple home healthcare devices. The mean upload rate for each lifelog item was ranging from 67.4% (fetal movement) to 85.3% (physical activity), and the total number of data points was over 6 million. Biospecimens, including maternal plasma, serum, urine, saliva, dental plaque and cord blood, were collected for multiomics analysis.Future plansLifelog and multiomics data will be used to construct a time-course high-resolution reference catalogue of pregnancy. The reference catalogue will allow us to discover relationships among multidimensional phenotypes and novel risk markers in pregnancy for the future personalised early prediction of pregnancy complications.

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SummaryObjectives: The aim of this study is to establish a sleep monitoring method that can classify sleep into four stages in an unconstrained manner using a polyvinylidene fluoride (PVDF) sensor for continuous and accurate estimation of sleep stages.Methods: The study participants consisted of 12 normal subjects and 13 obstructive sleep apnea (OSA) patients. The physiological signals of the subjects were unconstrainedly measured using the PVDF sensor during polysomnography. The respiration and body movement signals were extracted from the PVDF data. Rapid eye movement (REM) sleep was estimated based on the average rate and variability of the respiratory signal. Wakefulness was detected based on the body movement signal. Variability of the respira -tory rate was chosen as an indicator for slow-wave sleep (SWS) detection. Sleep was divided into four stages (wake, light, SWS, and REM) based on the detection results.Results: The performance of the method was assessed by comparing the results with a manual scoring by a sleep physician. In an epoch-by-epoch analysis, the method classified the sleep stages with an average accuracy of 70.9 % and kappa statistics of 0.48. No significant differences were observed in the detection performance between the normal and OSA groups.Conclusions: The developed system and methods can be applied to a home sleep monitoring system.

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In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors’ decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers—doctors and nurses—in this field.

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We aimed to evaluate sleep and sleep-related physiological parameters (heart rate variability and glucose dynamics) among evacuees by experimentally recreating the sleep environment of evacuation shelters and cars. Nine healthy young male subjects participated in this study. Two interventions, modeling the sleep environments of evacuation shelters (evacuation shelter trial) and car seats (car trial), were compared with sleep at home (control trial). Physiological data were measured using portable two-channel electroencephalogram and electrooculogram monitoring systems, wearable heart rate sensors, and flash glucose monitors. Wake after sleep onset (WASO) and stage shift were greater in both intervention trials than the control trial, while rapid-eye movement (REM) latency and non-rapid eye movement (NREM) 1 were longer and REM duration was shorter in the evacuation shelter trial than the control trial. Glucose dynamics and power at low frequency (LF.p) of heart rate variability were higher in the car trial than in the control trial. It was confirmed that sleep environment was important to maintain sleep, and affected glucose dynamics and heart rate variability in the experimental situation.

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Abstract Introduction The gold standard of sleep measurement has been laboratory polysomnography (PSG). However, electrodes and cables can cause discomfort, and exposure to an unfamiliar environment can cause the “first-night effect.” Difficulty falling asleep or maintaining sleep, poor sleep quality, and nightmares are some of the key clinical symptoms observed among individuals with psychiatric disorders. Those suffering from sleep disorders often present with symptoms of discontent with regard to sleep quality, timing, and quantity, and these symptoms have an adverse impact on function and quality of life. A minimally invasive technique would be preferable in patients with psychiatric disorders, who tend to be sensitive to environmental change. Accordingly, we evaluated the performance of a single-channel electroencephalography (EEG)-based sleep monitoring system in patients with psychiatric disorders. Methods Fifty-nine patients undergoing PSG were enrolled in this study. Single-channel EEG sleep monitoring was performed simultaneously with PSG. PSG and the EEG recordings were used to evaluate sleep parameters, such as total sleep time (TST), sleep efficiency, rapid eye movement (REM) sleep, light sleep (stages N1 and N2), and deep sleep (stage N3). Correlation analysis was used to evaluate the agreement on sleep parameters and attributing factors to the inaccuracies of the single-channel EEG recording. Results TST, sleep efficiency, REM sleep duration, and non-REM sleep duration of the single-channel EEG-based sleep monitoring showed a significant correlation with those of PSG. Lower sleep efficiency, a decrease in REM sleep, and increases in waking after sleep onset, arousal index, and apnea/hypopnea index were associated with the difference of sleep parameters between the two methods. Conclusion Among patients with psychiatric disorders who are sensitive to environmental change single-channel EEG sleep monitoring would be a useful technique to objectively evaluate sleep quality. Support Collaboration study with The KAITEKI Institute, Inc.

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Airway occlusion in awake humans produces a somatosensory evoked response called the respiratory-related evoked potential (RREP). In the present study, 29 channel evoked-potential recordings were obtained from seven men who were exposed to 250-ms inspiratory airway occlusions during wakefulness, stage 1, stage 2, and slow-wave sleep. The RREP recorded during wakefulness was similar to previous reports, with the unique observation of an additional short-latency positive peak with a mean latency of 25 ms. Short-latency RREP components were maintained in non-rapid-eye-movement (NREM) sleep. The clearly seen N1 vertex and late positive complex components during wakefulness were markedly attenuated during NREM sleep, and two large negative components (N300 and N550) dominated the sleep RREP. These findings indicate the maintenance of central nervous system monitoring of respiratory afferent information during NREM sleep, presumably to facilitate protective arousal responses to pathophysiological respiratory phenomena.

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Remote monitoring of vital signs for studying sleep is a user-friendly alternative to monitoring with sensors attached to the skin. For instance, remote monitoring can allow unconstrained movement during sleep, whereas detectors requiring a physical contact may detach and interrupt the measurement and affect sleep itself. This study evaluates the performance of a cost-effective frequency modulated continuous wave (FMCW) radar in remote monitoring of heart rate and respiration in scenarios resembling a set of normal and abnormal physiological conditions during sleep. We evaluate the vital signs of ten subjects in different lying positions during various tasks. Specifically, we aim for a broad range of both heart and respiration rates to replicate various real-life scenarios and to test the robustness of the selected vital sign extraction methods consisting of fast Fourier transform based cepstral and autocorrelation analyses. As compared to the reference signals obtained using Embla titanium, a certified medical device, we achieved an overall relative mean absolute error of 3.6% (86% correlation) and 9.1% (91% correlation) for the heart rate and respiration rate, respectively. Our results promote radar-based clinical monitoring by showing that the proposed radar technology and signal processing methods accurately capture even such alarming vital signs as minimal respiration. Furthermore, we show that common parameters for heart rate variability can also be accurately extracted from the radar signal, enabling further sleep analyses.

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Abstract Introduction This study was performed to investigate the differences in blood pressure among different groups of snoring children and among different sleep stages.In recent years, the incidence of OSAS in children has increased year by year. Blood pressure research of OSAS children can better understand the occurrence of OSAS related complications. Early detection and intervention of blood pressure changes in children with OSAS can effectively reduce the incidence of cardiovascular disease in adulthood and lower the disease burden. Methods Habitually snoring children (snoring frequency of ≥3 nights per week) aged 3to 11 years were recruited from Beijing Children’s Hospital, Capital Medical University from 1 January 2017 to 30 June 2018. All children underwent polysomnography, and their blood pressure was monitored and calculated by the pulse transit time. The children were divided into those with primary snoring (PS), mild obstructive sleep apnea syndrome (OSAS), and moderate to severe OSAS according to their obstructive apnea-hypopnea index (OAHI). Results In total, 140 children were recruited. Ninety-seven had PS, 24 had mild OSAS, and 19 had moderate to severe OSAS. There were no differences in age, sex, or body mass index z-score among the groups. Statistically significant differences were found in the OAHI, oxygen desaturation index 3%, respiratory arousal index, and lowest oxygen saturation among the three groups. Children with moderate to severe OSAS had higher systolic and diastolic blood pressure than those with mild OSAS and PS (P &lt; 0.001). In all children, systolic and diastolic blood pressure was higher in the rapid eye movement (REM) sleep stage than in the non-REM sleep stage (P &lt; 0.05). Conclusion Children with moderate to severe OSAS had higher blood pressure than those with PS and mild OSAS. Blood pressure in the REM sleep stage was higher than that in other sleep stages in all groups of children. Support The Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority (XTYB201807);Capital Health Research and Development of Special Funding (2018-1-2091);National Key Research and Development Plan (2017YFC0112502)

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Overnight analysis of tidal breathing flow volume (TBFV) loops, recorded by impedance pneumography (IP), has been successfully applied in the home monitoring of children with wheezing disorders. However, little is known on how sleep physiology modifies the relationship between TBFV profiles and wheeze. We studied such interactions in wheezing infants. Forty-three infants recruited because of recurrent lower airway symptoms were divided into three groups based on their risk of asthma: high (HR), intermediate (IR), or low (LR). Sedated patients underwent infant lung function testing including assessment of airway responsiveness to methacholine at the hospital and a full-night recording of TBFV profiles at home with IP during natural sleep. Overnight TBFV indexes were estimated from periods of higher and lower respiration variability, presumably belonging to active [rapid eye movement (REM)] and quiet [non-REM (NREM)] sleep, respectively. From 35 valid recordings, absolute time indexes showed intrasubject sleep phase differences. Peak flow relative to time and volume was lower in HR compared with LR only during REM, suggesting altered expiratory control. Indexes estimating the concavity/convexity of flow decrease during exhalation suggested limited flow during passive exhale in HR compared with IR and LR, similarly during NREM and REM. Moreover, during REM convexity was negatively correlated with maximal flow at functional residual capacity and methacholine responsiveness. We conclude that TBFV profiles determined from overnight IP recordings vary because of sleep phase and asthma risk. Physiological changes during REM, most likely decrease in respiratory muscle tone, accentuate the changes in TBFV profiles caused by airway obstruction. NEW & NOTEWORTHY Impedance pneumography was used to investigate overnight tidal breathing flow volume (TBFV) indexes and their interactions with sleep phase [rapid eye movement (REM) vs. non-REM] at home in wheezing infants. The study shows that TBFV indexes vary significantly because of sleep phase and asthma risk of the infant and that during REM the changes in TBFV indexes caused by airway obstruction are accentuated and better associated with lung function of the infant.

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Abstract Introduction Wearable electroencephalogram (EEG) monitoring has a remarkable potential, it is safe, scalable and can track neural signatures for long periods. One such signature is the power spectra of non-rapid-eye-movement (NREM) sleep which has been shown to demonstrate a trait-like characteristic. Changes in personalized signatures has been associated with biomarkers of Alzheimer’s disease and is of great interest for early detection and clinical management. This work investigates monitoring of signatures using a wearable device that records EEG from the ear (ear-EEG) and compares the intra- and inter-individual similarity of the neural signatures with that from central scalp-EEG. Methods We initiated a two phased in-home study, monitoring 20 subjects for 4 nights (A), followed by a delayed but continued monitoring of 10 subjects for 12 nights (B). In A, subjects wore a dry-electrode ear-EEG system and a partial PSG, in B the subjects wore only the ear-EEG system. Subjects were instructed to follow their usual time schedule and lifestyle. Sleep stages were scored manually according to AASM in A and automatically in B. The grand average power spectra of NREM2 sleep were computed and log-transformed prior to calculating the cosine similarity for determination of the intra- and inter-individual similarity. Results The ear-EEG and scalp-EEG analysis showed that mean intra-individual similarity was higher than mean inter-individual similarity. Permutation tests indicate that the observed mean difference is statistically significant p&lt;0.01 for both montages. Comparing the distributions of intra-individual similarities for ear-EEG and scalp-EEG, the observed mean difference is statistically significant p&lt;0.05, in favor of a more stable ear-EEG signature. Comparing ear-EEG signatures between A and B, considering nights from A as reference, all subjects from B were most similar with its own reference signature. Considering signatures from individual nights the accuracy paring subjects from A and B were 88% correct. Conclusion Nocturnal ear-EEG measures trait-like characteristics as reliable as scalp-EEG. The neural signature is stable over time within healthy subjects and demonstrated its ability as a personalized signature. Support (if any):

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Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.

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The critical closing pressure (Pcrit) is the airway pressure at which the airway collapses and reflects the anatomical contribution to the genesis of obstructive sleep apnea. Pcrit is usually determined during non-rapid eye movement sleep at night, but has been determined under midazolam sedation during the day in the absence of sleep stage monitoring. Indeed, little is known about the effects of midazolam on sleep architecture. Moreover, deeper sedation with midazolam can decrease upper airway muscle activity and increase collapsibility compared with natural sleep. Pcrit under sedation has not been systematically compared with the usual method performed during natural sleep. Therefore, this study aimed to test the hypothesis that Pcrit following low doses of midazolam during the day would be comparable to Pcrit measured during natural sleep in the same patient. Fifteen men (age 54 ± 10 yr, body mass index 30 ± 4 kg/m2) with obstructive sleep apnea underwent a baseline standard overnight polysomnogram (apnea-hypopnea index 38 ± 22 events/h, range: 8–66 events/h), and Pcrit was determined during natural sleep and following midazolam. Sleep induction was obtained with low doses of midazolam (2.4 mg, range 2.0–4.4 mg), and sleep architecture was comparable to natural sleep. Natural sleep and induced sleep Pcrit were similar (−0.82 ± −3.44 and −0.97 ± 3.21 cmH2O, P = 0.663) and closely associated (intraclass correlation coefficient = 0.92; 95% confidence interval, 0.78–0.97, P < 0.001). Natural and midazolam-induced Pcrit correlated with obstructive sleep apnea severity, indicating that both Pcrit measures provided meaningful physiological information. Pcrit determined during the day with sleep induction is similar to natural overnight sleep and is a valid alternative approach in which to determine Pcrit.

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Abstract Introduction Although polysomnography (PSG) remains the gold standard for sleep assessment in a lab setting, non-EEG signals such as respiration and motion are directly affected by sleep stages and can be used for sleep stage prediction. Importantly, these signals can be obtained in a low-cost and unobtrusive manner, allowing for large scale and longitudinal data collection in a home environment. The Circadia C100 System (FDA 510(k) clearance expected Q1 2020) is a novel ‘nearable’ device that uses radar for contactless monitoring of respiration and motion. The current study aims to validate the performance of the associated sleep analysis algorithm. Methods A total of 41 nights of sleep data were recorded from 33 healthy participants using the device, alongside PSG. Data were recorded both in a sleep lab and home environment. PSG data were scored by RPSGT-certified technicians. Respiration and movement features were extracted, and machine learning algorithms were developed to perform sleep stage classification and predict sleep metrics. Algorithms were trained and validated on PSG data using cross-validation. Results An epoch-by-epoch true positive rate of 56.2%, 79.4%, 55.5% and 72.6% was found for ‘Wake’, ‘REM’, ‘Light’ and ‘Deep’ respectively. No statistical differences in performance were found between home-recorded and lab-recorded contactless data. Mean absolute error of total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE) was 13.2 minutes, 11.3 minutes and 3%, respectively. The contactless monitor was found to outperform both medical grade and clinical grade actigraphy based devices: The Philips Actiwatch Spectrum Plus and the Fitbit Alta HR. Conclusion Current results are encouraging and suggest that the contactless monitor could be used for long-term sleep assessment and continuous evaluation of sleep therapy outcomes. Further clinical validation work is ongoing in subjects diagnosed with sleep disorders such as obstructive sleep apnea. Support -

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Abstract Introduction Sleep problems during adolescence are increasingly common and have been associated with adverse physical and psychological health outcomes. Efforts to improve insufficient sleep among adolescents have resulted in increased sleep knowledge and temporary enhancements in sleep hygiene. Good sleep hygiene is established through the development of daily routines that support healthy sleep. Wearable technology offers a potential solution whereby adolescents can acquire and manage healthy sleep habits. In this study, we are co-designing with adolescents a prototype intervention using wearable technology to promote sustained improvements in their sleep hygiene. Methods Guided by participatory design approaches, the ongoing multi-phase mixed methods study is currently being conducted in a metropolitan area in western Canada. In phase 1, sleep data is being collected from a sample of 30 adolescent-parent dyads using wearable sensors (Actigraphy watches) and self-report sleep measures (questionnaires about sleep quality, hygiene, and beliefs and attitudes, as well as their general health) over a 10-day period. In phases 2 and 3, individual interviews and iterative user interface design sessions will be conducted with 25 adolescents. Results To date, thirteen adolescents-parent dyads (13-17 years, 9 females; 39-56 years, 11 females) have completed phase 1 of our study. Data analysis is currently being conducted to evaluate sleep onset/offset, total sleep time, wake after sleep onset, sleep efficiency, and sleep schedule differences between adolescents and their parents. Ten adolescents have completed individual interviews in phase 2 of the study. Preliminary qualitative data suggests that youth are aware of the importance of sleep to their overall health. However, they struggle with identifying credible information to act on from the various and sometimes conflicting sources (e.g. online, friends, family). Conclusion We anticipate that co-designing a wearable solution with adolescents will lead to a sleep intervention that is more relevant, persuasive, and useful in supporting their sleep health. Support This work is supported by the Sensor Technology in Monitoring Movement STiMM Program.

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Abstract Introduction A role for vasoactive intestinal peptide (VIP) in promoting rapid eye movement (REM) sleep has been suggested, but the anatomical location of the neurons that release VIP to promote REM sleep has not been identified. Here, we investigated the role of VIP-containing cell groups in the ventromedial preoptic area (VMPOVIP) in sleep-wake regulation. The VMPO has also previously been implicated in thermoregulation and the febrile response. Methods We first investigated the native firing activity of VMPOVIP neurons, over repeated sleep-wake cycles, using in vivo fiber photometry in VIP-ires-Cre mice. We next examined the afferent and efferent profile of this cell group using conditional retrograde (pseudotyped modified rabies) and anterograde (adeno-associated viral vector-based) tracers. We finally utilized a chemogenetic strategy to selectively activate VMPOVIP neurons cells while monitoring electroencephalogram/electromyogram activity and core body temperature, in order to determine their role in sleep-wake and thermoregulatory control. Results We found that VMPOVIP cells were predominantly and strikingly REM-active, that they received many synaptic inputs from surrounding hypothalamic regions (including the ventromedial hypothalamus, dorsomedial hypothalamus and the arcuate nucleus), and that they targeted established sleep-wake nodes, such as the ventrolateral preoptic nucleus, tuberomammillary nucleus, lateral hypothalamus and ventrolateral periaqueductal gray area. To our surprise, chemogenetic activation of the VMPOVIP cell population had little effect upon all measures of sleep-wake analysed and no effect upon core body temperature. Conclusion We conclude that VMPOVIP neurons do not promote REM sleep per se. However, their REM-active profile and anatomical connectivity suggest that these neurons may play a functional role in generating certain cardinal features of REM sleep, which is an active focus of on-going research in our laboratory. Support SRSF CDA #016-JP-17 to A.V. and NS073613, NS092652 and NS103161 to P.M.

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Nasal high flow (NHF) reduces minute ventilation and ventilatory loads during sleep but the mechanisms are not clear. We hypothesised NHF reduces ventilation in proportion to physiological but not anatomical dead space.11 subjects (five controls and six chronic obstructive pulmonary disease (COPD) patients) underwent polysomnography with transcutaneous carbon dioxide (CO2) monitoring under a metabolic hood. During stable non-rapid eye movement stage 2 sleep, subjects received NHF (20 L·min−1) intermittently for periods of 5–10 min. We measured CO2 production and calculated dead space ventilation.Controls and COPD patients responded similarly to NHF. NHF reduced minute ventilation (from 5.6±0.4 to 4.8±0.4 L·min−1; p<0.05) and tidal volume (from 0.34±0.03 to 0.3±0.03 L; p<0.05) without a change in energy expenditure, transcutaneous CO2 or alveolar ventilation. There was a significant decrease in dead space ventilation (from 2.5±0.4 to 1.6±0.4 L·min−1; p<0.05), but not in respiratory rate. The reduction in dead space ventilation correlated with baseline physiological dead space fraction (r2=0.36; p<0.05), but not with respiratory rate or anatomical dead space volume.During sleep, NHF decreases minute ventilation due to an overall reduction in dead space ventilation in proportion to the extent of baseline physiological dead space fraction.

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Abstract Introduction Narcolepsy is a sleep disorder characterized by excessive daytime sleepiness, sleep paralysis, hallucinations, and in some cases episodes of cataplexy. Results from animal studies indicate the involvement of deficient orexin transmission in narcolepsy which can be circumvented by the activation of histaminergic neurons. SUVN-G3031 is a potent and selective histamine H3 receptor inverse agonist with hKi of 8.7 nM and shows less than 50% inhibition at 1 µM against 70 other targets. SUVN-G3031 exhibited excellent pharmaco*kinetic properties and brain penetration in preclinical species. Oral administration of SUVN-G3031 produces significant increase in histamine, dopamine and norepinephrine levels in the rat cortex. Long-term safety studies in animals have been successfully completed without any concern for further development of SUVN-G3031. In the present study, the effects of SUVN-G3031 were evaluated in orexin knockout mice, a reliable animal model of narcolepsy as a proof-of-concept study for the treatment of narcolepsy with and without cataplexy. Methods Male orexin knockout mice (10 - 15 weeks old, 25 - 35 g at the time of surgery) were implanted with telemetric device for simultaneous monitoring of electroencephalography (EEG) and electromyography. Animals were allowed surgical recovery of 3 weeks prior to EEG recording. Effects of SUVN-G3031 (3 and 10 mg/kg, p.o.) were evaluated during active period of animals. Results SUVN-G3031 produced significant increase in wakefulness with concomitant decrease in non-rapid eye movement sleep in orexin knockout mice. SUVN-G3031 also significantly decreased the number of cataplectic episodes in orexin knockout mice. Conclusion Results from the current preclinical study provide a strong basis for the utility of SUVN-G3031 for the treatment of narcolepsy with and without cataplexy. SUVN-G3031 is currently being evaluated in a Phase 2 study as monotherapy for the treatment of narcolepsy with and without cataplexy (ClinicalTrials.gov Identifier: NCT04072380). Support None

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Abstract Introduction Central sleep apnea (CSA) syndrome is defined when five or more central apneas and/or hypopneas are present per hour of sleep, more than 50% of all respiratory events. CSA usually occur during NREM stage and rarely during REM. CSA is important to recognize because of complications ranging from frequent nighttime awakenings,sleepiness to adverse cardiovascular outcomes. We present a 40 year old female patient with rare CSA during REM sleep and dream enactment. Report of Case 40yo African American female with history of loud snoring, witnessed sleep apnea, and daytime fatigue. She reported nightmares, sleep talking, and acting out her dreams without injury. Epworth sleepiness score was 5 /24. Her past medical history is significant for depression and anxiety. She has no history of head trauma, no neurologic or cardiovascular disorders. Her medications include fluoxetine and,quetiapine. She denied substance use, narcotic use, or alcohol use. Her level 1 sleep study showed predominantly REM-associated central sleep apneas which is rare. She also was observed to have loss of REM sleep muscle atonia suggestive of REM Behavior disorder. Her sleep architecture was atypical with decreased N3 sleep stage. REM sleep duration was adequate. She was noted to have loss of REM muscle atonia based on AASM guidelins elevated chin EMG, excessive transient muscle activity, and witnessed movement during REM stage via video monitoring. During the study, she had an apnea/hypopnea index (AHI) of 13.1 per hour of sleep, Central apneas were predominantly noted during REM stage, 10 per hour, comprised of 50% of her respiratory events. The minimum SpO2 value with CSA was 94%. She had normal sinus rhythm. Her sleep was fragmented. A total arousals were 28.4/hour,and 7.9/hour were respiratory arousals, and the rest were spontaneous arousals. An echocardiogram showed normal left ventricular ejection fraction of 55 to 60 %. Her room air arterial blood gas was normal with PaC02 of 37 mmHg. MRI of the brain/brainstem was ordered given her atypical REM sleep. She had no acute intracranial abnormalities. There is a non specific finding of a low lying cerebellar tonsils without evidence of Chiari I malformation. Conclusion Our patient has rare idiopathic central apnea in REM stage and is third case reported. She also has loss of muscle atonia during REM with dream enactment which is also rare in her age group. Injury precaution advised.

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Clinical depression is accompanied by changes in sleep patterning, which is controlled in a circadian fashion. It is thus desirable that animal models of depression mirror such diurnally-specific state alterations, along with other behavioral and physiological changes. We previously found several changes in behavior indicative of a depression-like phenotype in offspring of rats subjected to repeated, variable prenatal stress (PNS), including increased locomotor activity during specific periods of the circadian cycle. We, therefore, investigated whether PNS rats also exhibit alterations in sleep/wakefulness behavior around the change from light-to-dark phase. Control and PNS Sprague–Dawley rats were implanted with electrodes for continuous monitoring of electroencephalic activity used to determine behavioral state. The distribution of slow-wave sleep (SWS), rapid eye movement sleep (REMS) and wakefulness was compared for periods before and after lights were turned off, between baseline conditions and after exposure to an acute stressor. Both REMS and SWS amounts were increased in PNS rats relative to control animals in the beginning of the dark phase. REMS changes were due to an increase in REMS bout number, rather than in bout duration. During this circadian time period, we did not find any sex differences in the state changes. These results indicate that PNS affects baseline sleep patterning in both male and female rats around active-phase onset.

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Abstract Introduction Wearable, multisensory consumer devices that estimate sleep are prevalent and hold great potential. Most validated actigraphic prediction studies of sleep stages (SS) have only used low resolution (30 sec) data and the Cole-Kripke algorithm. Other algorithms are often proprietary and not accessible or validated. We present an automatic, data-driven deep learning algorithm that process raw actigraphy (ACC) and photoplethysmography (PPG) using a low-cost consumer device at high (25Hz) and low resolution to predict SS and to detect sleep disordered breathing (SDB) events. Methods Our automatic, data-driven algorithm is a deep neural network trained and evaluated to predict SS and SDB events on 236 recordings of ACC data from a wrist-worn accelerometer and PPG data from the overlapping PSG. The network was tested on raw ACC and PPG data, which was collected at 25 Hz using the HUAMI Arc2 wristband from 39 participants that underwent a nocturnal polysomnography (PSG). Results Overall accuracy (Acc), recall (Re), specificity (Sp), and kappa (κ) per subject on the test dataset the prediction of wake, NREM, REM was Acc=76.6%, Re=72.4%, Sp=78.0%, kappa=0.42. On average, we found a 7 % higher performance using the raw sensor data as input instead of processed, low resolution inputs. PPG was especially useful for REM detection. The network assigned 55.6% of patients to the correct SDB severity group when using an apnea-hypopnea index above 15. Conclusion Current results show that SS prediction is significantly improved when using the raw sensor data; it indicates that the system holds promise as a potential pervasive monitoring device for patients with chronic sleep disorders. In contrast the system did not show potential as a sleep apnea screening tool. Additional studies are ongoing to examine the effects of pathology such as sleep apnea and periodic leg movement on SS prediction. Support Technical University of Denmark; University of Copenhagen, Copenhagen Center for Health Technology, Klarman Family Foundation.

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There exists a technological momentum towards the development of unobtrusive, simple, and reliable systems for long-term sleep monitoring. An off-the-shelf commercial pressure sensor meeting these requirements is the Emfit QS. First, the potential for sleep apnea screening was investigated by revealing clusters of contaminated and clean segments. A relationship between the irregularity of the data and the sleep apnea severity class was observed, which was valuable for screening (sensitivity 0.72, specificity 0.70), although the linear relation was limited ( R 2 of 0.16). Secondly, the study explored the suitability of this commercial sensor to be merged with gold standard polysomnography data for future sleep monitoring. As polysomnography (PSG) and Emfit signals originate from different types of sensor modalities, they cannot be regarded as strictly coupled. Therefore, an automated synchronization procedure based on artefact patterns was developed. Additionally, the optimal position of the Emfit for capturing respiratory and cardiac information similar to the PSG was identified, resulting in a position as close as possible to the thorax. The proposed approach demonstrated the potential for unobtrusive screening of sleep apnea patients at home. Furthermore, the synchronization framework enabled supervised analysis of the commercial Emfit sensor for future sleep monitoring, which can be extended to other multi-modal systems that record movements during sleep.

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To test the hypothesis that occlusive apneas result from sleep-induced periodic breathing in association with some degree of upper airway compromise, periodic breathing was induced during non-rapid-eye-movement (NREM) sleep by administering hypoxic gas mixtures with and without applied external inspiratory resistance (9 cmH2O X l-1 X s) in five normal male volunteers. In addition to standard polysomnography for sleep staging and respiratory pattern monitoring, esophageal pressure, tidal volume (VT), and airflow were measured via an esophageal catheter and pneumotachograph, respectively, with the latter attached to a tight-fitting face mask, allowing calculation of total pulmonary system resistance (Rp). During stage I/II NREM sleep minimal period breathing was evident in two of the subjects; however, in four subjects during hypoxia and/or relief from hypoxia, with and without added resistance, pronounced periodic breathing developed with waxing and waning of VT, sometimes with apneic phases. Resistive loading without hypoxia did not cause periodicity. At the nadir of periodic changes in VT, Rp was usually at its highest and there was a significant linear relationship between Rp and 1/VT, indicating the development of obstructive hypopneas. In one subject without added resistance and in the same subject and in another during resistive loading, upper airway obstruction at the nadir of the periodic fluctuations in VT was observed. We conclude that periodic breathing resulting in periodic diminution of upper airway muscle activity is associated with increased upper airway resistance that predisposes upper airways to collapse.

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Bruxism is a sleep disorder in which the patient clinches and gnashes their teeth. Bruxism detection using traditional methods is time-consuming, cumbersome, and expensive. Therefore, an automatic tool to detect this disorder will alleviate the doctor workload and give valuable help to patients. In this paper, we targeted this goal and designed an automatic method to detect bruxism from the physiological signals using a novel hybrid classifier. We began with data collection. Then, we performed the analysis of the physiological signals and the estimation of the power spectral density. After that, we designed the novel hybrid classifier to enable the detection of bruxism based on these data. The classification of the subjects into “healthy” or “bruxism” from the electroencephalogram channel (C4-A1) obtained a maximum specificity of 92% and an accuracy of 94%. Besides, the classification of the sleep stages such as the wake (w) stage and rapid eye movement (REM) stage from the electrocardiogram channel (ECG1-ECG2) obtained a maximum specificity of 86% and an accuracy of 95%. The combined bruxism classification and the sleep stages classification from the electroencephalogram channel (C4-P4) obtained a maximum specificity of 90% and an accuracy of 97%. The results show that more accurate bruxism detection is achieved by exploiting the electroencephalogram signal (C4-P4). The present work can be applied for home monitoring systems for bruxism detection.

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Abstract Introduction Narcolepsy is a chronic sleep disorder characterized by overwhelming daytime drowsiness, sudden attacks of sleep and sometimes accompanied by cataplexy. Although the orexin deficiency is considered to be the primary cause of this disorder, lot of attention has been diverted on targeting histaminergic neurotransmission by blockade of histamine H3 receptor (H3R). Samelisant (SUVN-G3031) is one of the potent and selective H3R inverse agonist currently being evaluated in a Phase 2 study as monotherapy for the treatment of narcolepsy with and without cataplexy (ClinicalTrials.gov Identifier: NCT04072380). In the current research work, Samelisant was evaluated for neurotransmitter changes in rats and sleep EEG in orexin knockout mice, a reliable proof-of-concept study for treatment of excessive daytime sleepiness and cataplexy in narcolepsy. Methods Binding affinity of Samelisant towards human and rat histamine H3R was evaluated in in-vitro radioligand binding assay and functionality in GTP□S assay. Effect of Samelisant was studied in (R)-α-methyl histamine induced dipsogenia. In rat brain microdialysis, Samelisant was evaluated for its effects on modulation of neurotransmitters like histamine, dopamine and norepinephrine. Male orexin knockout mice were implanted with telemetric device for simultaneous monitoring of electroencephalography (EEG) and electromyography. Effects of Samelisant (3 and 10 mg/kg, p.o.) were evaluated during active period of animals. Results Samelisant is an inverse agonist at histamine H3 receptors with hKi of 8.7 nM and showed minimal binding against over 70 target sites. Samelisant produced significant increase in histamine, dopamine and norepinephrine levels in cortex. Samelisant produced no change in the striatal and accumbal dopamine levels in rats, suggesting no propensity to induce abuse liability. Samelisant blocked R-α-methyl histamine induced water intake and produced dose dependent increase in tele-methylhistamine levels in various brain regions and in cerebrospinal fluid of male Wistar rats. Samelisant produced significant increase in wakefulness with concomitant decrease in non-rapid eye movement sleep in orexin knockout mice. Samelisant also significantly decreased number of cataplectic episodes in orexin knockout mice. Conclusion Samelisant is an inverse agonist at histamine H3 receptor and results from the preclinical studies presented here provide a strong evidence for the potential utility of Samelisant in the treatment of narcolepsy with and without cataplexy. Support (if any):

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We examined whether growth hormone-releasing hormone (GHRH) may promote non-rapid eye movement (NREM) sleep via activation of GABAergic neurons in the preoptic area. Male Sprague-Dawley rats were implanted with EEG, EMG electrodes and a unilateral intracerebroventricular cannula. Groups of rats received injections (3 μl icv) with gonadotropin-releasing hormone (GHRH) (0.1 nmol/100 g body wt) or equal volume of physiological saline at the onset of the dark period and were permitted spontaneous sleep for 90 min. Separate groups of rats were sleep deprived by gentle handling for 90 min, beginning at the time of GHRH or saline injection, at the onset of the dark period. Other groups of rats received intracerebroventricular octreotide (somatostatin analog OCT) injections, intracerebroventricular injection of one of two doses of competitive GHRH antagonist, or intracerebroventricular saline injection at light onset and were then permitted 90 min spontaneous sleep-waking. Rats were killed immediately after the 90-min sleep/wake monitoring period. Brain tissue was processed for immunohistochemistry for c-Fos protein and glutamic acid decarboxylase (GAD). Single c-Fos and dual Fos-GAD cell counts were determined in the median preoptic nucleus (MnPN), and in the core and the extended parts of the ventrolateral preoptic nucleus (cVLPO and exVLPO). Intracerebroventricular GHRH elicited a significant increase in NREM sleep amount. Double-labeled Fos+GAD cell counts were significantly elevated after GHRH injection in the MnPN and VLPO in both undisturbed and sleep-deprived groups. OCT and GHRH antagonist significantly decreased NREM sleep amount compared with control rats. OCT injection increased single c-Fos-labeled cell counts in the MnPN, but not in the VLPO. Double-labeled cell counts were significantly reduced after OCT and the high dose of GHRH antagonist injection in all areas examined. These findings identify GABAergic neurons in the MnPN and VLPO as potential targets of the sleep-regulatory actions of GHRH.

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BACKGROUND: Chiari malformations (CM) may result in the appearance of REM sleep behavior disorder (RBD) and sleep apnea syndrome (SAS) that can be considered markers of brain stem dysfunction. PURPOSE: To evaluate the frequency of RBD and SAS in patients with CM type I and II. METHOD: Were evaluated 103 patients with CM by means of full night polysomnography. Were scoring different sleep stages, frequency of abnormal movements (through video monitoring) and abnormal respiratory events. RESULTS: Of the 103 patients, 36 showed CM type I and 67 CM type II. Episodes of RBD were observed in 23 patients. Abnormal apnea-hypopnea index (AHI) was observed in 65 patients. CONCLUSION: The high rate of RBD suggests that this parassomnia and the increased frequency of central sleep apnea episodes, may be considered as a marker of progressive brain stem dysfunction.

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Background Population aging is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased health care expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults. Objective In this work we aim to evaluate which contactlessly measurable sleep parameter is best suited to monitor perceived and actual health status changes in older adults. Methods We analyzed real-world longitudinal (up to 1 year) data from 37 community-dwelling older adults including more than 6000 nights of measured sleep. Sleep parameters were recorded by a pressure sensor placed beneath the mattress, and corresponding health status information was acquired through weekly questionnaires and reports by health care personnel. A total of 20 sleep parameters were analyzed, including common sleep metrics such as sleep efficiency, sleep onset delay, and sleep stages but also vital signs in the form of heart and breathing rate as well as movements in bed. Association with self-reported health, evaluated by EuroQol visual analog scale (EQ-VAS) ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to objective, real-world health incidents was investigated through manual retrospective case-by-case analysis. Results Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed—measured by the number toss-and-turn events—as the most predictive sleep parameter (t score=–0.435, P value [adj]=<.001). Case-by-case analysis further substantiated this finding, showing that increases in number of body movements could often be explained by reported health incidents. Real world incidents included heart failure, hypertension, abdominal tumor, seasonal flu, gastrointestinal problems, and urinary tract infection. Conclusions Our results suggest that nightly body movements in bed could potentially be a highly relevant as well as easy to interpret and derive digital biomarker to monitor a wide range of health deteriorations in older adults. As such, it could help in detecting health deteriorations early on and provide timelier, more personalized, and precise treatment options.

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Abstract Introduction Periodic neck myoclonus during Sleep (PNMS) is a movement disorder of sleep characterized by sudden myoclonic flexion or version of the head that manifest during REM and NREM sleep. While its finding has been attributed to a normal physiologic phenomenon, to the best of our knowledge, our case represents the first report of with PNMS attributed to sleep disordered breathing with resolution using CPAP Report of Case A 22 y/o male with no significant clinical history was referred for evaluation of snoring and excessive sleepiness. Nocturnal polysomnogram coupled with expanded EMG montage demonstrates evidence of upper airway resistance syndrome (UARS), characterized by frequents respiratory effort-related arousals (RERAS), primarily during REM sleep associated with arousals. The majority of these events resulted in sudden myoclonic movements of the neck and head that were associated with arousals and sleep fragmentation. PNMS manifested in the PSG as a flexion myoclonic motor artifact lasting 200-800 ms during REM sleep with an associated EEG arousal. The overall Respiratory Disturbance Index (RDI) was12/hr. The subsequent application of CPAP at a setting of 5-6 cm resolved these movements supporting this origin as a phenomenon of sleep-state instability. Conclusion While previous investigators have explained PNMS as an incidental finding or one common in patients with RBD, our case highlights a potential new mechanism for their appearance. This case helps shed more light on the origin of PNMS as a secondary phenomenon related to sleep state instability due to sleep disordered breathing given the temporal association with RERAS and dramatic resolution with CPAP therapy.

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Monitoring of respiration and body movements during sleep is a part of screening sleep disorders related to health status. Nowadays, thermal-based methods are presented to monitor the sleeping person without any sensors attached to the body to protect privacy. A non-contact respiration monitoring based on thermal videos requires visible facial landmarks like nostril and mouth. The limitation of these techniques is the failure of face detection while sleeping with a fixed camera position. This study presents the non-contact respiration monitoring approach that does not require facial landmark visibility under the natural sleep environment, which implies an uncontrolled sleep posture, darkness, and subjects covered with a blanket. The automatic region of interest (ROI) extraction by temperature detection and breathing motion detection is based on image processing integrated to obtain the respiration signals. A signal processing technique was used to estimate respiration and body movements information from a sequence of thermal video. The proposed approach has been tested on 16 volunteers, for which video recordings were carried out by themselves. The participants were also asked to wear the Go Direct respiratory belt for capturing reference data. The result revealed that our proposed measuring respiratory rate obtains root mean square error (RMSE) of 1.82±0.75 bpm. The advantage of this approach lies in its simplicity and accessibility to serve users who require monitoring the respiration during sleep without direct contact by themselves.

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Polysomnography and cardiorespiratory (respiratory) sleep monitoring are the most common diagnostic methods for respiratory sleep disorders. Polysomnography traditionally takes the place of the “gold” standard for detection of all types of respiratory events since its inception. Currently, cardiorespiratory monitoring of sleep is becoming more widespread as a diagnostic method with a minimum set of parameters for determining respiratory events during sleep. The increased use of cardiorespiratory (respiratory) monitoring of sleep is due to 2 reasons: the increased need for diagnosis due to the wide occurrence of respiratory disorders in the population and the conditions of the method (simple use, the need for a sleep laboratory, cheaper cost). However, the method is not indicated to all patients. Potential limitations for cardiorespiratory monitoring of sleep are the lack of sleep recording (information about the structure of sleep and reactions of sleep to respiratory disorders), monitoring of the study by medical personnel, and absence of body position sensor. These factors influence the assessment of the severity of the disease and the verification of certain forms of the disease. Currently, new methods of screening sleep apnea have been formed, based on modern innovative technologies and available in practical medicine. These include the determination of the presence of respiratory events be ECG Holter monitoring during sleep, the recognition of snoring and respiratory events in sleep from an audiometric signal recording and the determination of the probability of apnea with the help of registration movements during sleep (actigraphy).

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We studied four patients with end stage renal disease treated by continuous ambulatory peritoneal dialysis (CAPD) who complained of chronic insomnia and fatigue. Overnight polysomnography showed a disorder of sleep maintenance that was associated with nocturnal myoclonus. Sleeppromoting substance(s), Factor S-like material, that was extracted from the patient's dialysate effluent, was somnogenic and pyrogenic in a rabbit bioassay. Another proposed sleep substance, lnterleukin -I, isolated by gel filtration, also was present in the dialysate effluents of all patients. Altered metabolism and/or loss of these substances from the effluent may contribute to the chronic insomnia and fatigue of CAPD patients. End-stage renal disease (ESRD) patients are restored to medical “health” by dialysis, but commonly they complain of depression, fatigue, and insomnia. Profound sleep disturbances have been described (1–4) and are associated with nocturnal myoclonus (5). These sleep-related, periodic, involuntary leg movements occur at intervals of 20 to 40 sec and disrupt the continuity of sleep (6). In their review of sleep-wakefulness disorders associated with nocturnal myoclonus Coleman et al (7), suggest that a chronic sleep-wake disturbance may lead to these periodic movements during sleep rather than the converse. However, the physiologic mechanism for the involuntary leg movements during sleep remains unknown. Endogenous substances that promote sleep -neuromodulators, hypnotoxins have been investigated for many years (8). One of these, Factor S, has been extracted from human urine (9) and rabbit brain (10). Factor S derived from urine has been identified as a muramyl tetrapeptide with a molecular size of 922 daltons (11). Picomole quantities of this substance administered intracerebroventricularly were sufficient to induce excess slow-wave sleep (SWS) in rabbits for 6 or more hours (11). Some chemical analogues to Factor S, such as NAc-Mur-L-ala-D-isogln -a synthetic adjuvant called muramyl dipeptide or MDP, also were found to be somnogenic (12, 13). MDP and other related muramyl peptides also are pyrogenic and immunostimulatory (14). These responses could be elicited either directly by these synthetic compounds or through the leukocytic monokine interleukin-l (IL-l) (15), whose synthesis and release can be stimulated by muramyl peptides (16). IL-l, a polypeptide of about 15,000 daltons (17, 18), has the capacity to promote SWS (19). Recently IL-l has been demonstrated in astrocytes exposed to endotoxin (20). Thus astrocyte-derived IL-l may mediate certain brain functions, such as sleep. This paper will show that the sleep physiology of patients undergoing continuous ambulatory peritoneal dialysis (CAPD) is severely disrupted with noctumal myoclonus, i.e., sleep-related, involuntary, periodic leg movements. Further, dialysis fluids obtained from these patients contain a Factor S-like material and IL-l. It is possible that, in ESRD patients, altered metabolism and/or loss of the above-mentioned sleep-promoting substances may contribute to chronic insomnia and daytime fatigue.

Abstract:

According to current trends in healthcare sensing technologies, we describe a textile-based pressure sensing matrix that can be integrated in the mattress of a smart bed to characterize sleeping posture/movement of a subject and to extract breathing activity. The pressure mapping layer is developed as a matrix of 195 piezoresistive sensors, it is entirely made of textile materials, and it is the basic component of a smart bed that can perform sleep analysis, can extract physiological parameters, and can detect environmental data related to subject’s health. In this paper, we show the principle of the pressure mapping layer and the architecture of the dedicated electronic system that we developed for signal acquisition. In addition, we describe the algorithms for posture/movement classification (dedicated artificial neural network) and for extraction of the breathing rate (frequency domain analysis). We also perform validation of the system to quantify the accuracy/precision of the posture classification and the statistical analysis to compare our breathing rate estimation with the gold standard.

Abstract:

Abstract Introduction Polysomnogram (PSG) monitoring, including pulse oximetry, is the current diagnostic standard in sleep medicine. However, potentially confounding aspects of PSG testing include: test site other than the subject’s normal bed, distracting sensors and wires, subjective interpretation of complex recorded signals, and limited sensitivity to relevant phenomena. There is currently an unmet need for a sleep test that is more clinically effective than PSG, and that can be administered in the subject’s normal sleeping environment. Additionally, confirmation that home therapy has been optimized cannot be achieved by PSG titration. Methods A recent proof of concept (POC) study of the armband-wearable Reveal Cellular Energy Monitor (CE monitor) directly compared its data product, Cellular Energy Index (CEi), with PSG data. Scoring methods were adapted from AASM guidance for interpretation of PSG data. At-home recording with the CE monitor was also performed prior to and following PSG studies. At-home incremental adjustment of APAP settings and mask selection was documented with CE monitoring and compared with the information recorded by the home APAP machine. Results The comparison of the POC data consistently found the CE monitor to be more sensitive and responsive to hypoxic stress than the PSG pulse oximeter during primary snoring. Obstructive and central apnea events were detected by both, but the CE monitor provided finer resolution of the breath-by-breath effort of breathing compared with PSG RIP and nasal sensors. At-home CE monitor optimization of therapy was documented to often differ from the settings and mask selection determined by PSG titration, and resulted in ‘normal’ sleep breathing data. Conclusion All diagnostically-relevant physiologic responses detected by PSG were also detected by the CE monitor. Evidence of cellular hypoxia in the skin, by CE monitor, was consistently recorded during prolonged periods of ‘primary snoring;’ i.e., SpO2 is less sensitive to hypoxic stress during sleep than CEi. Breath-by-breath effort is detected by the CE monitor. Support (if any) The POC study costs at UCSF were paid by Reveal Biosensors, Inc.

Abstract:

✓ Monitoring physiological changes in the brain parenchyma has important applications in the care of neurosurgical patients. A technique is described for measuring extracellular neurochemicals by cerebral microdialysis with simultaneous recording of electroencephalographic (EEG) and single-unit (neuron) activity in selected targets in the human brain. Forty-two patients with medically intractable epilepsy underwent stereotactically guided implantation of a total of 423 intracranial depth electrodes to delineate potentially resectable seizure foci. The electrodes had platinum alloy contacts for EEG recordings and four to nine 40-µm microwires for recording single-unit neuron activity. Eighty-six electrodes also included microdialysis probes introduced via the electrode lumens. During monitoring on the neurosurgical ward, electrophysiological recording and cerebral microdialysis sampling were performed during seizures, cognitive tasks, and sleep—waking cycles. The technique described here could be used in developing novel approaches for evaluation and treatment in a variety of neurological conditions such as head injury, subarachnoid hemorrhage, epilepsy, and movement disorders.