Night Recordings Research Articles

Wavelet-Detected Changes in Nocturnal Brain Electrical Activity in Patients with Non-Motor Disorders Indicative of Parkinson's Disease.

Background/Objectives-Parkinson's disease (PD) is the second most common neurodegenerative disorder caused by the destruction of neurons in the substantia nigra of the brain. Clinical diagnosis of this disease, based on monitoring motor symptoms, often leads to a delayed start of PD therapy and control, where over 60% of dopaminergic nerve cells are damaged in the brain substantia nigra. The search for simple and stable characteristics of EEG recordings is a promising direction in the development of methods for diagnosing PD and methods for diagnosing the preclinical stage of PD development. Methods-42 subjects participated in work, of which 4 female/10 male patients were included in the group of patients with non-motor disorders, belonging to the risk group for developing PD (median age: 62 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 143 and 80)/(median age: 68 years, height: 170 cm, weight: 73.9 kg, pulse: 75, BPsys and BPdia: 143 and 82). The first control group of healthy participants included 6 women (median age: 33 years, height: 161 cm, weight: 66 kg, pulse: 80, BPsys and BPdia: 110 and 80)/8 men (median age: 36.3 years, height: 175 cm, weight: 69 kg, pulse: 78, BPsys and BPdia: 120 and 85). The second control group of healthy participants included 8 women (median age: 74 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 145 and 82)/6 men (median age: 51 years, height: 172 cm, weight: 72.5 kg, pulse: 74, BPsys and BPdia: 142 and 80). Wavelet oscillatory pattern estimation is performed on patients' nocturnal sleep recordings without separating them into sleep stages. Results-Amplitude characteristics of oscillatory activity in patients without motor disorders and the prodromal PD stage are significantly reduced both in terms of changes in the number of patterns and in terms of their duration. This pattern is especially pronounced for high-frequency activity, in frequency ranges close to 40 Hz. Conclusions-The success of the analysis of the electrical activity of the brain, performed over the entire duration of the night recording, makes it promising to further use during daytime monitoring the concept of oscillatory wavelet patterns in patients with non-motor disorders, belonging to the risk group for developing PD. The daytime monitoring system can become the basis for developing screening tests to detect neurodegenerative diseases as part of routine medical examinations.

Threshold-dependent association between non-rapid eye movement obstructive sleep apnea and interictal epileptiform discharges: A hospital study.

Obstructive sleep apnea frequently coexists with epilepsy, potentially influencing its pathophysiology. However, the effect of obstructive sleep apnea severity on interictal epileptiform discharges is not well understood. To explore this, we studied 108 Asian patients with epilepsy who underwent single-night polysomnography. We utilized generalized linear models, adjusting for age, sex, epilepsy type (focal versus generalized), antiepileptic medication use and disease duration, to analyse the relationship between obstructive sleep apnea severity, as measured by the apnea-hypopnea index, and interictal epileptiform discharge frequency during non-rapid eye movement and rapid eye movement sleep. Our analysis revealed that severe obstructive sleep apnea (apnea-hypopnea index ≥ 30) was associated with a higher frequency of interictal epileptiform discharges during non-rapid eye movement sleep (p = 0.04), but no such association was observed during rapid eye movement sleep. Additionally, the frequency of interictal epileptiform discharges in non-rapid eye movement sleep was positively correlated with the wake time between sleep onset and offset (p = 0.03). Further studies are warranted to validate our findings across diverse ethnicities, and over multiple nights of sleep and interictal epileptiform discharge recordings.

Soundwaves in the North Atlantic: Detecting the offshore bats of Sable Island

As Sable Island National Park Reserve approaches a decade of protection under Parks Canada, new data on the island’s biodiversity is useful for informing management. An ultrasonic autonomous recording unit was deployed for 15 months over two years (October 5, 2015-January 8, 2017) to quantitatively characterize the presence of bats on Sable Island. A total of 1721 echolocation passes were identified across the 32 nights that bats were detected on the island (6.9% of total recording nights). Of the nights that bats were detected, Lasionycteris noctivagans (Silver-haired Bat) was present on 65% of nights, Myotis species were present on 53% of nights, Lasiurus borealis (Eastern Red Bat) was present on 25% of nights, and Lasiurus cinereus (Hoary Bat) was present on 6.25% of nights. All recordings were captured between late-September and early-December. Evidence of bat species on Sable Island, whether deliberately stopping during migration or brought there by external factors such as weather, highlights that the island may be more important for bats than previously thought. Keywords: bats, bioacoustics, migration, monitoring, Sable Island

Sleep Differences in Firefighters: Barracks vs. Home.

The aims were (1) to quantify firefighters' sleep via polysomnography, (2) to identify differences between sleeping in the barracks versus sleeping at home, and (3) to compare firefighter data to age-matched normative data. We expected significant differences between both the home and the barrack conditions as well as significant differences when both conditions were compared to normative data. 10 male firefighters completed 3 nights of polysomnography recordings (SleepProfilerTM (Advanced Brain Monitoring, Carlsbad, CA, USA)) counterbalanced in both their own beds or barracks. A one-way rmANOVA statistical analysis was used to determine differences in sleep values with a Bonferroni correction if a significant difference was found with significance set at p < 0.05. Three important variables, cortical arousals (p < 0.05), autonomic activations (p < 0.01), and spindle duration (p < 0.01), had differences that were statistically significant between sleep at home or in the barracks, with sleep in the barracks being more disturbed. Clinical differences were also observed between the home and barrack conditions and all sleep results were more deleterious when compared to normative data. The data demonstrates that firefighters show poor sleep quality and heavily impacted sleep architecture. This may be due to the effects of rotating shifts and occupational stress on the sleep-wake cycle. These results, when compared to age-matched normative data, show clinical manifestations of disturbed sleep in the firefighter population.

Sleep staging algorithm based on smartwatch sensors for healthy and sleep apnea populations

ObjectiveSleep stages can provide valuable insights into an individual's sleep quality. By leveraging movement and heart rate data collected by modern smartwatches, it is possible to enable the sleep staging feature and enhance users' understanding about their sleep and health conditions. MethodIn this paper, we present and validate a recurrent neural network based model with 23 input features extracted from accelerometer and photoplethysmography sensors data for both healthy and sleep apnea populations. We designed a lightweight and fast solution to enable the prediction of sleep stages for each 30-s epoch. This solution was developed using a large dataset of 1522 night recordings collected from a highly heterogeneous population and different versions of Samsung smartwatch. ResultsIn the classification of four sleep stages (wake, light, deep, and rapid eye movements sleep), the proposed solution achieved 71.6 % of balanced accuracy and a Cohen's kappa of 0.56 in a test set with 586 recordings. ConclusionThe results presented in this paper validate our proposal as a competitive wearable solution for sleep staging. Additionally, the use of a large and diverse data set contributes to the robustness of our solution, and corroborates the validation of algorithm's performance. Some additional analysis performed for healthy and sleep apnea population demonstrated that algorithm's performance has low correlation with demographic variables.

Digital sleep clinic: assessing efficacy of continuous positive airway pressure through sleep staging via connected devices: a study protocol

BackgroundObstructive sleep apnea (OSA) is a multisystemic chronic disease with disabling symptoms, cardiometabolic comorbidities and reduction in physical activity. Continuous positive airway pressure (CPAP) is the standard treatment for OSA. Only a few studies have characterized trajectories of sleep parameters upon initiation of CPAP and these are limited to one or two nights of polysomnographic recording in a sleep laboratory. This is due to the cost of carrying out these studies and poor tolerance by patients of multiple nights of polysomnographic recordings. No study has characterized sleep over multiple nights before and after CPAP initiation, assessing the multidimensional efficacy of CPAP on patient reported outcomes, objective and subjective sleep quality, oximetry, glucose control and physical activity.New digital technologies enable overnight sleep studies over several nights in the patient’s home, with a reliability of sleep characterization equivalent to polysomnographic recording.The primary aim of this study is to investigate objective slow wave sleep (SWS or N3) quality before CPAP and during the first month of the treatment.Secondary objectives are to assess changes in the following parameters before CPAP and during the first month of the treatment: other objective sleep parameters and sleep stages evolution (W, N1, N2 and REM), nocturnal oxygen desaturations, 24-h blood glucose profile, daily physical activity (the daily steps count), and patient reported outcomes.MethodsSeventy patients prescribed CPAP for OSA will be recruited at Grenoble Alpes University Hospital (France) and monitored for 5 weeks using validated innovative wearable connected devices (the Dreem 3 headband, a pedometer, an oximeter, and a continuous glucose sensor) enabling them to track their own sleep and physiological parameters at home before and after CPAP initiation.DiscussionBy pooling data from the CPAP telemonitoring and other connected devices we should be able to follow the multidimensional trajectories of patients after the initiation of CPAP. This will enable us to determine whether objective changes in sleep parameters in the first few weeks of CPAP treatment are associated with improvements in daytime sleepiness, quality of life, treatment adherence, glucose control and physical activity. The data will provide integrated markers of treatment efficacy and will allow adapted personalized management of OSA in the short and long-term.Trial registrationClinicaltrials (NCT05197855).

Alteration of circadian sleep-wake rhythm and salivary melatonin secretion in idiopathic/isolated REM sleep behavior disorder: Preliminary evidence

Objective/BackgroundIdiopathic/isolated REM sleep behavior disorder (iRBD) is widely regarded as an early sign of neurodegeneration leading to synucleinopathies. While circadian rhythm alterations in iRBD have been preliminarily demonstrated, evidence on melatonin secretion patterns in this clinical condition is limited. To address this knowledge gap, this exploratory study aimed to integrate salivary melatonin measurement with actigraphic monitoring in individuals with iRBD and age-matched healthy controls (HC) under real-life conditions. MethodsParticipants diagnosed with iRBD and HC underwent clinical evaluation and wore an actigraph for seven days and nights. Salivary melatonin concentrations were measured at five time points during the last night of recording. Comparative analyses were conducted on clinical data, actigraphic parameters, and melatonin levels between the two groups. ResultsiRBD participants (n = 18) showed greater motor (p < 0.01) and non-motor symptoms (p < 0.001), alongside disruptions in circadian sleep-wake rhythm compared to HC (n = 10). Specifically, actigraphy revealed a delayed central phase measurement (p < 0.05), reduced activity during the most active hours (p < 0.001), and decreased relative amplitude (p < 0.05). Total salivary melatonin concentration was significantly lower in iRBD (p < 0.05), with a slight but non-significant phase delay in dim light melatonin onset. ConclusionsThis exploratory study highlights a dysregulation of circadian sleep-wake rhythm coupled with reduced melatonin secretion in iRBD. Future research could add to these preliminary findings to evaluate novel treatment approaches to regulate the sleep-wake cycle and elucidate the implications of circadian dysregulation in the conversion from iRBD to neurodegeneration.

Inter-night variability of in-home, overnight pulse oximetry screening in an asymptomatic older adult population.

Obstructive sleep apnoea (OSA) is common, yet often undiagnosed. Self-administered, overnight pulse oximetry (OPO) could screen for OSA in asymptomatic, older populations. However, the inter-night variability of OPO in an asymptomatic, older population is unknown. We determined the inter-night variability of home OPO parameters in an older population and correlated with sleep questionnaires. Participants > 50 years without a diagnosis of OSA undertook home OPO for three consecutive nights and completed two sleep questionnaires (STOP-BANG (SBQ) and Epworth Sleepiness Score (ESS)). Analysis was performed with linear mixed models and Spearman's correlation coefficient. There was no difference in oxygen desaturation index (ODI), MeanSpO2, MinimumSpO2, and time spent with SpO2 < 90% (T90) across two or three nights (P ≥ 0.282). However, the variability of all parameters across nights increased with the magnitude of departure from normal values (P ≤ 0.002). All OPO parameters were associated with age (P ≤ 0.034) and body mass index (P ≤ 0.049). There was a weak correlation between three OPO parameters and SBQ (absolute ρ = 0.22 to 0.32; P ≤ 0.021), but not ESS (P ≥ 0.254). Inter-night variability of home OPO was minimal when values were near-normal in an older population. However, as values depart from normal, the inter-night variability increases, indicating the need for multiple night recordings. Low correlation to sleep questionnaires suggest the need for more robust OSA questionnaires in an asymptomatic population.

Short-Term Effects of 3D-Printed Occlusal Splints and Conventional Splints on Sleep Bruxism Activity: EMG–ECG Night Recordings of a Sample of Young Adults

Short-Term Effects of 3D-Printed Occlusal Splints and Conventional Splints on Sleep Bruxism Activity: EMG–ECG Night Recordings of a Sample of Young Adults

Are we getting enough sleep? Frequent irregular sleep found in an analysis of over 11 million nights of objective in-home sleep data

Evidence-based guidelines recommend that adults should sleep 7-9h/night for optimal health and function. This study used noninvasive, multinight, objective sleep monitoring to determine average sleep duration and sleep duration variability in a large global community sample, and how often participants met the recommended sleep duration range. Data were analyzed from registered users of the Withings under-mattress Sleep Analyzer (predominantly located in Europe and North America) who had ≥28 nights of sleep recordings, averaging ≥4 per week. Sleep durations (the average and standard deviation) were assessed across a ∼9-month period. Associations between age groups, sex, and sleep duration were assessed using linear and logistic regressions, and proportions of participants within (7-9hours) or outside (<7hoursor >9hours) the recommended sleep duration range were calculated. The sample consisted of 67,254 adults (52,523 males, 14,731 females; aged mean±SD 50±12years). About 30% of adults demonstrated an average sleep duration outside the recommended 7-9h/night. Even in participants with an average sleep duration within 7-9hours, about 40% of nights were outside this range. Only 15% of participants slept between 7 and 9hours for at least 5 nights per week. Female participants had significantly longer sleep durations than male participants, and middle-aged participants had shorter sleep durations than younger or older participants. These findings indicate that a considerable proportion of adults are not regularly sleeping the recommended 7-9h/night. Even amongthose who do, irregular sleep is prevalent. These novel data raise several important questions regarding sleep requirements and the need for improved sleep health policy and advocacy.

Exploring the Association Linking Head Position and Sleep Architecture to Motor Impairment in Parkinson's Disease: An Exploratory Study.

Patients with Parkinson's disease (PD) tend to sleep more frequently in the supine position and less often change head and body position during sleep. Besides sleep quality and continuity, head and body positions are crucial for glymphatic system (GS) activity. This pilot study evaluated sleep architecture and head position during each sleep stage in idiopathic PD patients without cognitive impairment, correlating sleep data to patients' motor and non-motor symptoms (NMS). All patients underwent the multi-night recordings, which were acquired using the Sleep Profiler headband. Sleep parameters, sleep time in each head position, and percentage of slow wave activity (SWA) in sleep, stage 3 of non-REM sleep (N3), and REM sleep in the supine position were extracted. Lastly, correlations with motor impairment and NMS were performed. Twenty PD patients (65.7 ± 8.6 y.o, ten women) were included. Sleep architecture did not change across the different nights of recording and showed the prevalence of sleep performed in the supine position. In addition, SWA and N3 were more frequently in the supine head position, and N3 in the supine decubitus correlated with REM sleep performed in the same position; this latter correlated with the disease duration (correlation coefficient = 0.48, p-value = 0.03) and motor impairment (correlation coefficient = 0.53, p-value = 0.02). These preliminary results demonstrated the importance of monitoring sleep in PD patients, supporting the need for preventive strategies in clinical practice for maintaining the lateral head position during the crucial sleep stages (SWA, N3, REM), essential for permitting the GS function and activity and ensuring brain health.

O044 Are we Sleeping Enough? Short and Irregular Sleep is Common in a Large Global Community Sample with Objective, Multi-night, In-home Sleep Data

Abstract Introduction Evidence-based guidelines recommend that adults should sleep 7-9hrs per/night. This study used multi-night, in-home, objective sleep duration monitoring to determine how often a large global community sample meets the recommended sleep duration range. Methods Data were analysed from registered users of the Withings Sleep Analyzer under-mattress device who had ≥28 nights of sleep recordings, averaging ≥4 per week. Sleep durations were assessed as the average and standard deviation of sleep duration across a ~9-month period. Proportions of participants within (7-9hrs) or outside (&amp;lt;7hrs or &amp;gt;9hrs) the recommended sleep duration range were calculated. Associations between age groups, sex, and sleep duration were assessed using linear and logistic regressions. Results In total, data were analysed from 67,254 adults (52,523 males, 14,731 females; aged Mean±SD 50±12y). About 30% of the sample showed an average sleep duration outside the recommended 7-9hrs per/night. Even in participants with an average sleep duration within 7-9hrs, about 40% of recorded nights were outside this range, and only 15% of participants slept between 7-9h for at least 5 nights per/week. Females had significantly longer sleep durations than males, and middle-aged participants had lower sleep durations than younger and older participants. Discussion A considerable proportion of adults in this sample were not regularly sleeping the recommended 7-9hrs per/night. Even amongst those who do, irregular sleep is prevalent. These novel data raise several important questions regarding sleep requirements, lifestyle factors, and the need for improved sleep health policy and advocacy.

AS3-SAE: Automatic Sleep Stages Scoring Using Stacked Autoencoders

Purpose: Sleep is a subconscious state, and the brain is active during it. Automatic classification of sleep stages can help identify various diseases. In recent years, automatic sleep monitoring using deep learning networks has attracted the attention of researchers.&#x0D; Materials and Methods: In this paper, a deep learning type neural network called Stacked Autoencoders (SAEs) is used for automatically classifying sleep stages. SAEs are a kind of neural network with encoder and decoder blocks. The function of these networks is similar to the human brain and is capable of automatically processing signals; also SAEs are robust to noise. To prove the efficiency of this network, in addition to examining the effect of various biological signals such as Electrocardiogram (ECG) and Electroencephalogram (EEG) on the performance of sleep stage classification, Sleep Heart Health Study (SHHS) and ISRUC standard databases have been used, which include night recordings of 30 and 10 healthy humans, respectively. &#x0D; Results: The accuracy of classifying 2 to 6 classes by SHHS database are 0.995, 0.983, 0.9780, 0.9688, 0.961, and on ISRUC database accuracies are 0.996, 0.994, 0.9511, and 0.9431. Moreover, the proposed network can classify wake, deep sleep, and light sleep using the ECG signal (acc = 0.75, kappa = 0.69).&#x0D; Conclusion: In the review of the results, it is concluded that sleep stages classification based on EEG signal has better results, still acquisition of ECG signal and its acceptable results can be a good alternative to use. In addition to its high ability of the proposed method to detect sleep stages, this network is robust to noise, which is very necessary and important for the clinical processing of sleep signals.

Efficacy and safety of closed-loop control system for type one diabetes in adolescents a meta analysis

This meta-analysis compares the efficacy and safety of Closed-Loop Control (CLC) to Sensor-Augmented Insulin Pump (SAP) for adolescent patients with Type 1 Diabetes Mellitus (T1DM). Eleven randomized-controlled trials were included with a total of 570 patients, from a total of 869 articles found adhering to PRISMA guidelines. The efficacy of the therapies were evaluated from the day, night and during physical activities monitoring of the of the mean blood glucose (BG), Time In Range (TIR), and Standard Deviation (SD) of the glucose variability. The safety measure of the therapies, was assessed from the day and night recording of the hypoglycemic and hyperglycemic events occurred. Pooled results of comparison of mean BG values for day, night and physical activities, − 4.33 [− 6.70, − 1.96] (P = 0.0003), − 16.61 [− 31.68, − 1.54] (P = 0.03) and − 8.27 [− 19.52, 2.99] (P = 0.15). The monitoring for day, night and physical activities for TIR − 13.18 [− 19.18, − 7.17] (P < 0.0001), − 15.36 [− 26.81, − 3.92] (P = 0.009) and − 7.39 [− 17.65, 2.87] (P = 0.16). The day and night results of SD of glucose variability was − 0.40 [− 0.79, − 0.00] (P = 0.05) and − 0.86 [− 2.67, 0.95] (P = 0.35). These values shows the superiority of CLC system in terms of efficacy. The safety evaluation, of the day, night and physical activities observations of average blood glucose goal hypoglycemic events − 0.54 [− 1.86, 0.79] (P = 0.43), 0.04 [− 0.20, 0.27] (P = 0.77) and 0.00 [− 0.25, 0.25] (P = 1.00) and hyperglycemic events − 0.04 [− 0.20, 0.27] (P = 0.77), − 7.11 [− 12.77, − 1.45] (P = 0.01) and − 0.00 [− 0.10, 0.10] (P = 0.97), highlights the commendable safety factor of CLC. The CLC systems can be considered as an ideal preference in the management of adolescents with type 1 diabetes to be used during a 24 h basis.

Real-Time Detection of Sleep Arousals with a Head-Mounted Accelerometer.

Wearable electroencephalography (EEG) enables real-time interactions with the sleeping brain in real-life settings. An important parameter to monitor during these interactions are sleep arousals, i.e. temporary increases in EEG frequency, that compose sleep dynamics, but are challenging to detect without delay. We describe the development of an EEG- and accelerometer(ACC)-based sensing approach to detect arousals in real-time. We investigated the ability of these sensing modalities to timely and accurately detect arousals. When evaluated on 6 nights of mobile recordings, ACC had a median real-time delay of 2 s and was therefore better suited for an early detection of arousals than EEG (4.7 s). The detection performance was independent of sleep stages, but worked better on longer arousals. Our results demonstrate that a head-mounted ACC might be a cost-effective and easy-to-integrate solution for arousal detection where short delays are important or EEG signals are not available.

Development of generalizable automatic sleep staging using heart rate and movement based on large databases

PurposeWith the advancement of deep neural networks in biosignals processing, the performance of automatic sleep staging algorithms has improved significantly. However, sleep staging using only non-electroencephalogram features has not been as successful, especially following the current American Association of Sleep Medicine (AASM) standards. This study presents a fine-tuning based approach to widely generalizable automatic sleep staging using heart rate and movement features trained and validated on large databases of polysomnography.MethodsA deep neural network is used to predict sleep stages using heart rate and movement features. The model is optimized on a dataset of 8731 nights of polysomnography recordings labeled using the Rechtschaffen & Kales scoring system, and fine-tuned to a smaller dataset of 1641 AASM-labeled recordings. The model prior to and after fine-tuning is validated on two AASM-labeled external datasets totaling 1183 recordings. In order to measure the performance of the model, the output of the optimized model is compared to reference expert-labeled sleep stages using accuracy and Cohen’s κ as key metrics.ResultsThe fine-tuned model showed accuracy of 76.6% with Cohen’s κ of 0.606 in one of the external validation datasets, outperforming a previously reported result, and showed accuracy of 81.0% with Cohen’s κ of 0.673 in another external validation dataset.ConclusionThese results indicate that the proposed model is generalizable and effective in predicting sleep stages using features which can be extracted from non-contact sleep monitors. This holds valuable implications for future development of home sleep evaluation systems.

0443 Proposing a ‘projected apnea-hypopnea index (AHI)’ to mitigate intrinsic pitfalls of conventional AHI and testing its validity

Abstract Introduction The night-to-night variability of apnea-hypopnea index (AHI) can be explained by the fact that a person with OSA tends to spend less time in supine position and potentially less REM sleep, during which obstructive events could be a lot worse than in the other form. The primary aim of this study is to propose and test a “projected AHI” (pAHI) with which AHI can be mathematically adjusted for sleep position and sleep stage spent in a night of recording. Methods Using mathematical modeling explained in a separate abstract, a final pAHI formula was generated from polysomnography data of 337 subjects who exhibited all four sleep-types [combination of REM vs NREM and supine (SUP) vs non-supine (nSUP)] in one overnight study. A confusion matrix was generated using cutoffs of (p)AHI of 5 and hypothesizing pAHI being the gold standard and AHI being the test. Among subjects with borderline results (AHI of 5±3), sleep-type time and sleep-type AHI were compared between 4 groups of the confusion matrix to test the validity of the pAHI model. Results Out of 337 subjects with all four sleep-types, 119 subjects with AHI of 5±3 were identified and classified into a confusion matrix of no-OSA (43 TN, 10 FP), and OSA (13 FN, 53 TP). Actual sleep-type AHIs were in the order of increasing pAHI (TN, FP, FN, TP): 15.3, 29.2, 32.0, 34.8 for REMSUP; 4.00, 4.53, 4.73, 9.40 for REMnSUP; 2.83, 2.87, 9.38, 10.0 for NREMSUP; 0.44, 0.1, 1.06, 1.98 for NREMnSUP. Sleep time parameters were significantly different between no-OSA and OSA groups only when pAHI was used for diagnosis: %REMSUP (9.32 vs 5.39, p = 0.000009), %REMnSUP (8.08 vs 11.31, p = 0.002), %NREMSUP (48.13 vs 36.53, p = 0.001). %NREMnSUP (34.46 vs 46.77, p = 0.0002). Conclusion Without having to repeat a sleep study, projected AHI, by taking the effect of sleep position and sleep stages into account, can produce an AHI that mitigates night-to-night variability stemming from position and stage differences between nights. Projected AHI can be particularly useful in interpreting a sleep study with a borderline AHI result. Support (if any)

0263 Feasibility of Measuring Sleep Spindles Using a Wearable EEG Headband in Adolescents with Neurodevelopmental Disorders

Abstract Introduction There is compelling evidence that sleep spindles, electroencephalogram (EEG) signatures of non-rapid eye movement sleep, are reliable markers of thalamocortical dysfunction in Schizophrenia. While promising, this research is limited by traditional sleep EEG methods, which cultivate small, non-representative samples. Wearable EEG headbands could be used to study naturalistic sleep across several nights in individuals who cannot tolerate standard sleep assessments, but this has yet to be tested. We aim to test feasibility of using a dry-EEG headband to measure sleep spindles in adolescents with a neurodevelopmental disorder, 22q11.2 Deletion Syndrome (22q11DS). Methods Adolescents with 22q11DS (n=7; 3 Females; Age: 15-28 years) were asked to sleep in their own bed wearing a dry-EEG headband (Dreem 3). Feasibility of data acquisition was determined by the ability of the participants to record at least 3 consecutive nights. We performed visual artifact detection and examined the ability to detect sleep spindles using the well-validated Ferrarelli Spindle Detection Algorithm on the F7-O2 electrode. Results All subjects completed at least 3 nights of recording (range: 3-5 nights), suggesting the device was well-tolerated. The data contained minimal artifact; 17 out of the 22 nights available for analysis contained artifact in &amp;lt; 20% of sleep epochs. The best night contained artifact in only 6% of sleep epochs. Sleep spindles were successfully detected using an automatic detector (M= 1.92 spindles/minute). Conclusion This pilot study suggests dry-EEG headbands offer a feasible method to measure sleep spindles in individuals with neurodevelopmental disorders. This new technology could allow for scalable biomarkers of atypical neurodevelopment associated with psychiatric disorders. Support (if any) RO1 MH085953; U01MH10171; UCLA Center for Autism Research Pilot Grant; Dreem Coup de Coeur Award

0335 Persistence of Sleep Duration Phenotypes Across Nights of Laboratory and Home Sleep Recording

Abstract Introduction Vgontzas and colleagues have proposed dividing the population of persons with insomnia disorder into short (&amp;lt; 6 hours/night) and normal (&amp;gt; 6 hours/night) sleep duration phenotypes based on their objective sleep duration as measured by one or more nights of polysomnography (PSG). However, first night effects, homeostatic recovery mechanisms and the site where PSG is conducted may all affect the sleep duration observed on individual PSG nights. The current study evaluated the stability of objective sleep duration phenotyping across multiple laboratory and home-based PSG recordings. Methods Seventy-six adults (Mage = 51.8±16.5 yrs.; 50% women) with an insomnia disorder underwent a series of three PSG recordings in the sleep lab and three in their homes in a counterbalanced order. Participants chose their own bed and rising times each night to produce results representative of their usual sleep patterns. Participants were classified into short (ISS: TST &amp;lt; 6 hours) and normal (INS: TST &amp;gt; 6 hours) duration phenotypes using the mean sleep times across all nights. Results Twenty-eight participants were classified as the ISS phenotype and 48 were classified as the INS phenotype with equal numbers of men and women in each group. A linear mixed models analysis showed only significant main effects for phenotype classification (F = 119.72; p &amp;lt; .0001) and recording night (F = 4.29; p &amp;lt; .025). The ISS mean TST was 326.8 minutes whereas the INS mean TST was 396.8 minutes. Mean TST across both groups was significantly shorter on the first PSG night (Mean = 353.0 min.) across settings than on night 2 (Mean = 372.4 min.). The majority of the ISS group (78.6%) and INS group (77.1%) had TST &amp;lt; 6 hours and TST &amp;gt; 6 hours, respectively, on 4 or more nights of sleep recordings. Conclusion ISS and INS sleep duration phenotypes have relative stability in their objective sleep times across nights and recording sites. These findings support the growing literature suggesting the distinctiveness of these subtypes of insomnia disorder. Support (if any) Funding support from the Department of Veterans Affairs Merit Review Grant Program

0961 Applying Fractal Analysis Towards the Understanding of Long-COVID-Induced Insomnia

Abstract Introduction As part of the recent international COVID Sleep Study-II, long COVID syndrome was found to be associated with chronic insomnia. However, an article submitted for publication out of the Hôpital Hôtel-Dieu found no significant difference between long-COVID-related insomnia and standard insomnia with respect to routine polysomnographic (PSG) parameters. In light of these findings, we seek to better understand the difference between long COVID insomnia and standard chronic insomnia by conducting a fractal analysis of the raw PSG signals of our subjects. Methods 15 long COVID patients with complaints of chronic insomnia were included as subjects, as well as 34 matched controls without long COVID who had similar complaints of chronic insomnia. All participants underwent one night of polysomnographic recording. We perform our analysis on movements of the abdomen (as an approximation of deep breathing quality) and on EEG waves, considering that shortness of breath and cognitive dysfunction are commonly associated with long COVID. Building upon an existing repertoire of medical research using the fractal dimension (FD) of patients’ signal data to differentiate pathologic and physiologic processes, we make use of Detrended Fluctuation Analysis (DFA) to calculate the FD of the abdomen (FDA) and of an aggregated EEG wave (FDE) for each patient. Results We find a decrease in the FDA (p=0.036) and increase in the FDE (p=0.012) of long COVID insomniacs with respect to those of our control group. These results are shown to be significant after applying a Holm-Bonferroni correction for multiple hypotheses. Conclusion Despite our small data sample, an in-depth analysis of PSG signals shows significant differences in the fractal nature of long-COVID-related insomnia with respect to standard chronic insomnia. Our results furthermore match our expectations, given that related research outside of the COVID domain indicates a decrease in the FD of muscle movements and an increase in the FD of EEG waves in unhealthy individuals. Further research could employ more participants in order to extend our results to other data channels outside of EEG signals and the abdomen. Support (if any)