bluuebunny/crossref_metadata_2025 huggingface dataset:
0290 Sex Differences: Nicotine Use and Abstinence Effects on Sleep Architecture in Female and Male C57BL/6J Mice
Authors: Aki Sidney , Brown Morgan , Stitzel Jerry , Mathews Hunter
Published: April 2024

Introduction

Nicotine withdrawal symptoms play a major role in relapse rates, and sleep disturbances occurring during withdrawal are a reliable predictor of relapse. Additionally, research has shown that both nicotine withdrawal symptoms and sleep disturbances are sex dependent. Thus, understanding sex differences in sleep architecture during abstinence is critical for treating nicotine addiction. This study aims to investigate sex differences in sleep architecture in female and male C57BL/6J mice undergoing nicotine abstinence.

Methods

EEG/EMG implantation devices were surgically implanted to capture sleep data (n = 9 males, n = 11 females). Mice were individually housed in a 12-hour light/dark schedule. During pre-nicotine baseline, mice received a vehicle solution containing 0.2% saccharin in water. To induce nicotine dependence, 200 μg/ml nicotine was added to the saccharin solution and oral nicotine exposure occurred for 14 days. Abstinence was initiated by removing nicotine from the drinking solution after the 14 day exposure. EEG/EMG was scored for 2 days during baseline and averaged and scored for 4 days during oral nicotine exposure and averaged. EEG/EMG for days 1, 2, and 5 were analyzed individually to assess the duration of any sleep disruptions. Sirenia software was used to determine sleep stages in 4 second epochs. A repeated measures ANOVA test was used along with Dunnett’s post-hoc test.

Results

Nicotine exposure reduced total sleep and NREM time in both females and males during 24-Hour and active phase measures. Abstinence induced a sleep fragmentation phenotype, as defined by an increase in sleep bout frequency with a decrease in sleep bout duration, during the inactive phase in both females and males –. Females had a much more robust sleep fragmentation. Females also exhibited NREM fragmentation.

Conclusion

Nicotine exposure has similar effects to total sleep time and NREM percent time during the 24—hour and active phase measures regardless of sex. Female mice experienced a much robust sleep fragmentation phenotype compared to male mice. NREM fragmentation, which underlined the greater sleep fragmentation phenotype, was unique to female mice.

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0291 Evaluating Sleep Quality Metrics Using Zero-Effort Technology: Implications for Public Health Dynamics
Authors: Kaur Jasleen , Oetomo Arlene , Chauhan Vivek , Morita Plinio
Published: April 2024

Introduction

Sleep quality is critical to human health and well-being, with implications for manifold physiological and psychological processes. The quality and reliability of the data due to recall bias and subjective interpretation often limit traditional methods of sleep data collection. This research presents a novel framework that can objectively measure and evaluate sleep quality using smart thermostats equipped with motion sensors, providing non-invasive and effortless sleep monitoring.

Methods

We leveraged the ecobee 'Donate Your Data' initiative, which collects data from smart home sensors, to analyze 8 Terabytes of data from 178,706 households. In our time-series model, sensor activation values were transformed into signals to model sleep features. We developed a data pipeline integrating data preprocessing, feature engineering, and various machine learning models. These models, such as RNN, VAE, K-means clustering, PCA, and Random Forest classifiers, were used to discern sleep quality indicators. These indicators included Absolute Sleep Duration, Normalized Disturbance Time, Wakeup Onset and Sleep Onset Time, Time in Room, and Sleep Efficiency (with and without onset time) derived from motion sensor data.

Results

Our findings show three distinct sleep quality clusters, with clear variations in sleep duration, disturbances, and efficiency. Cluster 0 profiled a pattern of fewer disturbances and higher sleep efficiency, whereas Cluster 1 indicated a moderate disturbance with prolonged sleep onset and wakeup duration. The lowest average sleep duration and varied disturbances characterized Cluster 2. Comparative analysis underscores the heterogeneity in sleep quality, highlighting the potential of Internet of Things (IoT) devices in identifying sleep patterns and contributing to sleep research without invasive monitoring. These clusters demonstrate the heterogeneity of sleep quality and showcase the potential of Internet of Things (IoT) devices in sleep pattern identification.

Conclusion

This novel approach shows smart thermostats as a viable data source for evaluating sleep quality, providing a new perspective on technology in health surveillance and paving the way for personalized sleep improvement strategies for sleep technologists and healthcare policymakers. Furthermore, integrating temperature data from ecobee datasets could deepen insights into the connection between indoor temperature and sleep, impacting health monitoring and policy in the context of increasing global temperatures from climate change.

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0293 Evaluation of a Single Point of Contact Chest-worn HSAT with ECG-based Atrial Fibrillation Detection
Authors: Goldstein Cathy , Ghanbari Hamid , Sharma Surina , Collop Nancy , Loring Zak , Walsh Colleen , Herreshoff Emily , Pollock Mark , Frankel David , Rosen Ilene
Published: April 2024

Introduction

Obstructive sleep apnea (OSA) is highly prevalent, underdiagnosed, and often comorbid with cardiac arrhythmias such as atrial fibrillation (AF). Existing home sleep apnea tests (HSATs) remain complex, often requiring multiple points of contact, and do not provide gold-standard ECG-based arrhythmia detection. A simplified HSAT with expanded capabilities for ECG-based arrhythmia detection could bridge this diagnostic gap, stimulate sleep-cardiology collaborations, and reduce patient burden; resulting in more comprehensive, patient-centered care. Here we performed a comparative PSG evaluation of the Sansa device (Huxley Medical, Inc.), a single point of contact HSAT worn on the chest that provides heart rate, oximetry, respiratory effort, total sleep time (TST), and ECG.

Methods

Patients undergoing PSG for suspected OSA were enrolled at three sleep laboratories. The chest-worn HSAT was used during overnight, level I PSG. Manually scored PSG (AASM manual 3.0) parameters were compared to the chest-worn HSAT’s associated parameters for AHI requiring hypopneas with 4% oxygen desaturation (sAHI4%), AHI requiring hypopneas with 3% oxygen desaturations (sAHI3%), TST, and classification of moderate-to-severe OSA (AHI≥15). AF and premature ventricular complexes (PVC) were visually compared between ECG derived from PSG and chest-worn HSAT.

Results

Enrollment included 101 patients (47% male, age 52±18 years, BMI 32±9 kg/m2, 27% Fitzpatrick skin tone 5-6). The chest-worn HSAT detected moderate-to-severe OSA with 90% sensitivity and 92% specificity (AHI4%, 37% prevalence) and 88% sensitivity and 80% specificity (AHI3%, 45% prevalence). Strong agreement was demonstrated for sAHI4% (-15.9 to 14.5 95% limits of agreement, LoA), sAHI3% (-23.6 to 20.7 95%-LoA), and TST (-62.0 to 87.7 95%-LoA). Preliminary visual review of ECG showed AF and/or PVCs in at least 15% of overnight recordings, in agreement with corresponding PSG ECG.

Conclusion

Non-flow-based HSATs to assess OSA are increasingly available; however, here we present the performance of a novel device to simultaneously diagnose OSA and AF (and other arrhythmias). Given the relevance of OSA treatment in successful AF management, these data highlight the capacity of a single point of contact chest-worn HSAT in the simultaneous diagnosis of both disorders toward downstream benefits of hastening OSA treatment to improve AF outcomes.

Support (if any)

National Science Foundation; Georgia Research Alliance; Huxley Medical, Inc.
0298 Comparing Youth-Reported, Caregiver-Reported and Fitbit Sleep Measures in a Large Early Adolescent Sample
Authors: Kiss Orsolya , Shaska Adrianna , Camacho Leticia , Harkness Anya , Tager Lucas , Fan Ruby , Perez-Amparan Evelyn , Kerr Erin , Arra Nicole , Durley Ingrid , Müller-Oehring Eva , Hasler Brant , Franzen Peter , Clark Duncan , Baker Fiona
Published: April 2024

Introduction

Healthy sleep is critical for the physical, emotional, and cognitive development of adolescents. Capturing the multidimensional components of sleep behavior in this age group remains a challenge, particularly when relying on retrospective questionnaires. This study compares sleep data collected via youth-report, caregiver-report and from Fitbit devices in a large, diverse sample of early adolescents.

Methods

This study analyzed data from the Adolescent Brain Cognitive Development (ABCD) Study, comprising 11,879 US adolescents (Year 2 age: 11−14 years). The participants self-reported their sleep period (from falling asleep to wake-up time) via the Munich Chronotype Questionnaire, while their caregivers completed the Children's Sleep Disturbance Scale. A subset (N = 4,282, Mean Age = 11.97 years, 51.19% female) also wore Fitbit Charge 2 devices for sleep tracking for 21 days, directly following the annual assessment. We evaluated the internal consistency of the questionnaires and employed Bland-Altman and interclass correlation analyses to compare self-reported and Fitbit measures of sleep period.

Results

We found acceptable internal consistency in the youth-reported (α = 0.71) and caregiver-reported (α = 0.83) sleep questionnaires. There was a greater discrepancy between caregivers and adolescents when adolescents reported sleep durations less than 7 hours: 38% of caregivers reported 7-8 hours and 35% reported 8-9 hours whereas only 15% reported < 7 hours. Compared to Fitbit measures adolescents generally estimated their sleep period with reasonable accuracy (ICC = 0.182 [0.15, 0.21] p<.001), displaying an average discrepancy of 40.5 minutes on weekdays.

Conclusion

The findings indicate that there is reasonable agreement between youth-report and Fitbit measures of sleep period in adolescents, opening up the possibility for assessing the complexity of sleep behavior. In this age group, caregivers tend to overestimate the adolescents' sleep duration, which highlights the importance of youth-reported sleep along with objective measures. This study contributes valuable insights into the methodology of sleep research and underscores the need for multi-dimensional approaches in assessing sleep patterns in adolescents.

Support (if any)

National Institute of Health: U01DA041022
0302 Sleep Staging Classification from Wearable Signals Using Deep Learning
Authors: Heneghan Conor , Gillard Ryan , Niehaus Logan , Schneider Logan , Guerard Marius
Published: April 2024

Introduction

Typical data derived from a wrist worn device include accelerometer and photoplethysmogram (PPG) sensor signals . These reflect underlying movement, heart rate, and vascular dynamics that contain sleep stage information. We investigated the ability of a deep learning network to map raw data from such sensors to estimated sleep stages defined by full polysomnography scoring.

Methods

A convolutional neural network (CNN) was proposed for application to raw PPG (green light at 25 Hz) and 3D accelerometer data (also sampled at 25 Hz). The CNN had 70 hidden layers and output labels were mapped to four classes (wake, light sleep, deep sleep, and REM sleep) where light sleep is defined as Stages N1 and N2. The CNN was pretrained using 1654 records of finger PPG data from the Multi-Ethnic Study of Atherosclerosis (MESA) sleep records. The system was then further trained and evaluated on an internal set of 184 records obtained from adults (mean age = 68) with corresponding scored PSG sleep stage labels. Data augmentation techniques were used to create additional training data. The system was then tested using a withheld data set of 16 records. The overall performance of the system was evaluated by calculating two stage (wake versus sleep) and four stage accuracy and Cohen’s kappa values (𝜅).

Results

The overall performance for two-stage wake/sleep classification was an accuracy of 0.94 and 𝜅=0.79. For four stage classification, the accuracy was 0.79 and 𝜅=0.66. A comparable figure for expert human scoring four-stage class is accuracy of 0.8-0.85 and 𝜅=0.7-0.75.

Conclusion

Raw accelerometer and PPG signals contain a significant amount of information related to underlying sleep stages, and can be trained to produce hypnograms which approach the accuracy of human scorers. This may provide utility for both multi-night clinical use and underlying research in sleep science.

Support (if any)

This research was funded by Google Inc.
0304 Attitudes Towards Mobile Technologies for Sleep and Circadian Health Promotion Among Military Personnel
Authors: Schmied Emily , Leslie Emily , Riggs David , Willing Maegan Paxton
Published: April 2024

Introduction

Mobile technologies, including wearable devices and mobile apps, have been widely promoted as potentially effective tools for the assessment and improvement of sleep and circadian health in populations at high risk for circadian disruption, such as military personnel. However, little research has been done to assess the perceived utility of these technologies within this population. The purpose of this study was to qualitatively assess attitudes towards the use of mobile technologies for the promotion of sleep health among active duty service members.

Methods

Five focus groups were conducted with active duty Marines (N = 30; 90% male, 10% female) as part of a needs assessment designed to identify current sleep-related needs and potential intervention approaches. Focus group transcripts were analyzed by two independent coders using applied thematic analysis to identify and interpret salient themes.

Results

Many participants reported currently using wearable devices capable of providing data on sleep health, including Apple watches and Fitbits, and some reported using popular sleep-focused apps. Despite the current use of these technologies, participants expressed ambivalence about future interventions that exclusively relied on apps or wearables to promote sleep and circadian health. Instead, participating Marines reported a desire for other approaches for promoting sleep health, with many describing a preference for in-person, small-group education sessions led by civilian facilitators. Participants reported that education sessions may increase motivation among Marines to improve their sleep hygiene behaviors or provide instruction on how to maximize the utility of wearable devices (e.g., “If they already have the smartwatch capability and you say, ‘Hey, this would be an excellent tool.’ I think that gives them some sort of metric to compare themselves to.”) Additionally, participants expressed a desire for organizational efforts to improve sleep health, including leadership training and scheduling interventions.

Conclusion

Study results indicate that wearables and other technological interventions for sleep may be acceptable among active duty Marines, but that they must be accompanied by other strategies to maximize their effectiveness in this population.

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0307 Correlations Between Subjective and Objective Estimations of Sleep Quantity and Quality from Wearable Devices
Authors: Cuellar Joseph , Gordy Prayag , Jameson Jason , Markwald Rachel , Chinoy Evan
Published: April 2024

Introduction

Beyond sleep quantity, another important factor for health and performance is sleep quality. While sleep quantity is reliably measured with objective methods like actigraphy, sleep quality is typically only assessed via subjective report, so it is still debated whether it can be estimated objectively. Many new-generation wearable devices have been found to equal or outperform actigraphy in sleep/wake measurement, but whether their estimates of sleep quantity or quality also correlate with subjective sleep estimates/ratings has yet to be established. Therefore, we explored whether subjective estimates/ratings of sleep (total sleep time [TST] and sleep quality) were correlated with objective estimates provided by commercial wearable devices.

Methods

In total, 33 healthy young adults participated in week-long home sleep studies. In Study 1, participants (n=21) habitually slept at night using a device set including Philips Respironics Actiwatch 2, Fatigue Science ReadiBand, Fitbit Inspire HR, Polar Vantage V Titan, and Oura Ring (OR). In Study 2, participants (n=12) with habitual irregular sleep schedules (i.e., some naps/daytime sleep) used a device set including Actiwatch 2, Garmin Fenix 7, and OR. For all sleep episodes, participants wore their devices and completed sleep diaries. Pearson correlations were calculated between devices and diaries for TST, and for sleep quality scores (0–100) from the OR algorithm compared with diary, Likert-type sleep quality ratings (1–5). P-values <.05 were considered statistically significant.

Results

Correlations between subjective and objective TST were significant for all devices. Likewise, OR sleep quality scores significantly correlated with subjective sleep quality ratings.

Conclusion

This analysis provides initial evidence for relationships between objective estimates of sleep quantity and quality from commercial wearable devices and subjective estimates/ratings from sleep diaries in healthy adults. While TST had previously been known to correlate between actigraphy and diaries, this had not been extensively tested in commercial wearables. Additionally, we found these relationships extended to OR’s sleep quality score. Such proprietary “sleep quality” scores are often the primary sleep-tracking metric displayed from new-generation wearables; however, scores are proprietary and without actigraphy equivalents. More testing is needed to explore whether these relationships extend to other populations/settings.

Support (if any)

Office of Naval Research, Code 34
0309 Concordance in Electroencephalography-Headband versus Polysomnography by Demographics and Cognitive Status
Authors: Miner Brienne , Pan Yulu , Cho Gawon , Burzynski Chase , Polisetty Lakshmi , Doyle Margaret , Iannone Lynne , Knauert Melissa , Gill Thomas , Yaggi Henry
Published: April 2024

Introduction

Polysomnography (PSG) can be burdensome and alternative measures are needed to advance sleep research. We previously reported that a novel electroencephalography (EEG)-headband outperformed actigraphy when compared to PSG among older persons with sleep complaints. The current study examined the concordance of the EEG-headband with PSG, and variations in concordance by gender, race, and cognitive status.

Methods

Sixty community-living adults ≥60 years with self-reported sleep complaints completed a 7-day home-based sleep protocol with a novel EEG-headband (Beacon Biosignals) and one night of home-based polysomnography (NOX-A1 Sleep System). We evaluated the concordance of total sleep time [TST] and wake after sleep onset [WASO] between PSG and the EEG-headband using paired t-tests and two-way mixed effects intraclass correlation coefficients (ICC). We examined variations in concordance by gender, race, and the presence of mild cognitive impairment [MCI] (< 13 on the Montreal Cognitive Assessment short form) using Fisher Z-transformation to assess between-group differences in ICCs.

Results

Mean age was 74 years, 65% were women, 33.3% were people of color, and 35% had MCI. The EEG-headband showed excellent concordance with PSG when measuring TST (absolute difference -9.14 minutes [95% Confidence Interval -23.17, 4.89]; ICC= 0.83 [0.73, 0.89]) and good concordance when measuring WASO (absolute difference -17.11 [-13.71, -2.50]; ICC= 0.64 [0.47, 0.77]). Concordance in TST was significantly different between men and women (ICCs of 0.91 vs 0.67, respectively; p=0.01), but did not vary by race and cognitive status. Concordance in WASO did not vary by gender, race, or cognitive status.

Conclusion

Among community-living older adults with sleep disturbances, the EEG-headband showed good to excellent concordance with PSG when measuring TST and WASO. The EEG-headband is an acceptable alternative to PSG, including among people of color or persons with MCI. Reduced concordance in TST among women may have been due to differences in fit and/or contact of the headband with the scalp.

Support (if any)

NIA (K76AG0749505, R03AG073991, P30AG021342)
0312 An At-home, Wireless, Sleep Monitoring System for Long-term, Reliable Sleep Assessment in Young and Older Adults
Authors: Saha Simran , Kim Hyeonseok , Nyan Chuu , Yeo Woonhong , Trotti Lynn Marie , Duarte Audrey
Published: April 2024

Introduction

Poor sleep quality increases from middle age onward with impacts on cognition, most notably episodic memory, and is associated with an increased risk of Alzheimer’s disease (AD). There is great interest in determining if sleep quality, and what specific neural features thereof, may serve as a non-invasive biomarker of memory decline and AD. Constraints of “gold standard” polysomnography assessments of sleep’s neural architecture, including bulky, wired, rigid, expensive, and uncomfortable equipment either in a lab or at home, make collecting multiple nights of sleep data prohibitive. As night-to-night sleep variability predicts poorer cognition, it is essential to sample multiple nights from the comfort of home. Existing wearable devices enable sleep monitoring at home but remain bulky and rigid, or offer poor signal quality and inaccurate sleep analysis.

Methods

We deployed, in cognitively unimpaired young (age 18-36) and older (ages 60-74) adults, our recently developed and validated, inexpensive, gel-free, nearly weightless, sleep monitoring patch that uses soft, skin-conformable self-adhesive materials containing multiple laser-cut electrodes, self-applied to the face for optimal usability and comfort.

Results

Participants reported being easily able to self-apply the reusable sleep patch and operate the Bluetooth tablet-based data acquisition software throughout seven nights. Manual scoring and automated classification-based scoring showed high agreement across all sleep stages (82.43%, kappa value = 0.74). Signal-to-noise estimates of slow wave activity (0.5-3.75 Hz) show remarkable stability over the 7 nights in both young (SNR range 26.71 dB-33.42 dB, Cronbach’s alpha 0.797) and older (SNR range 26.27 dB-28.42 dB, Cronbach’s alpha 0.846) adults, comparable to estimates obtained from optimal conditions in our university sleep lab. Consistent with results from polysomnography, older adults showed reduced slow wave (N3) sleep (Young: 7.13 %; Older: 2.88 %).

Conclusion

This study validates that the high comfort, wearable patch can measure physiological sleep data at the level of clinical standards in adults across the lifespan. Future work will use this system to assess sleep-dependent consolidation across ages from the comfort of one’s home and measure sleep variables longitudinally to identify those most indicative of cognitive decline.

Support (if any)

This work was supported by the Alzheimer’s Association # FA00000633 and NIA # R21AG064309
0316 Real-time Detection of Oxygen Desaturation Using Electroencephalogram Signals
Authors: Manjunath Shashank , Sathyanarayana Aarti
Published: April 2024

Introduction

Existing sleep apnea identification methods require polysomnograms, requiring patients schedule the exam and to sleep with many sensors attached to their body. Oxygen desaturation occurs during sleep in patients with sleep apnea, leading to poor rest and other significant complications. Existing work shows the potential of EEG signals as indicators of sleep apnea, identifying a correlation between EEG signals and sleep apnea diagnosis. However, further work is required to understand how blood oxygenation correlates with features in the brain. Recent advancements in signal processing allow EEG signal analysis to identify oxygen desaturation events in real time.

Methods

We leverage EEG signals from polysomnogram studies included in the Nationwide Children’s Hospital Sleep DataBank (NCHSDB) dataset for this study. We first break the data into 30-second epochs, then label epochs where the oxygen desaturation event (< 90% SpO2) encompasses all 30 seconds of the epoch as epochs containing desaturations. We furthermore label all epochs where no oxygen desaturation occurs as epochs without desaturations. We perform short-term Fourier transforms on each epoch of EEG data. We randomly select a training set of 20 subjects and a testing set of 20 subjects, different for each sleep state (NREM1, NREM2, NREM3, REM). We then train a ResNet based deep learning network on these EEG signals to predict which epochs contain an oxygen desaturation and which epochs do not contain an oxygen desaturation.

Results

Our model obtains a test balanced accuracy of 77.1% for NREM1 sleep, 58.9% for NREM2 sleep, 80.1% for NREM3 sleep, and 71.0% for REM sleep.

Conclusion

Our results indicate that machine learning models can identify a correlation between oxygen desaturation from EEG signals alone, indicating a potential biomarker for real-time identification of oxygen desaturation from neural signals. This provides a step towards easier identification of sleep apnea, requiring fewer sensors than a traditional polysomnogram.

Support (if any)

NCH Sleep DataBank was supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under Award Number R01EB025018. The National Sleep Research Resource was supported by the U.S. National Institutes of Health, National Heart Lung and Blood Institute (R24 HL114473, 75N92019R002).
0317 Advancing Home Sleep Testing: A Comparative Analysis of Type 2 New HST Technology vs. In-Lab PSG Validation Study
Authors: Fox Benjamin , Shihab Murad , Nassir Abed , Barnea Ofer , Tal Asher
Published: April 2024

Introduction

Currently, a typical home sleep test only measures respiratory signals, not actual sleep. Therefore, results may be inconclusive or false. Current practice also allows for one-night sleep study at home, that may also affect the results and render them inconclusive. Given these limitations, there is a growing need for a more convenient, accurate, and cost-effective method to diagnose sleep disorders at home. The method should allow patients to connect by themselves to the device at home and allow remote real-time monitoring. It should also provide the possibility for a study that spans over several nights.

Methods

Forty seven adults (mean ± SD age 52.2 ± 12.8 years, 19% females, body mass index 29.4 ± 5.3 kg/m2) used the DormoTech V-lab and simultaneously underwent a full PSG test using the Nox A1 system (K192469). Quantitative methods such as Bland–Altman plots, correlation analysis, and Passing-Bablok regression analysis were used to assess the extent of agreement between the Nox device and the Vlab device. The tests were manually scored based on recommended guidelines.

Results

The mean ± standard deviation apnea-hypopnea index (AHI) was 21.72 ± 24.17 events/h on the Vlab device, 21.50 ±23.86 events/h on in-laboratory PSG NoxA1 system with a p-value of 0.732. Bland-Altman analysis of AHI showed a mean difference of −0.193; the limits of agreement were −7.209 to 6.823 events/h. High agreements across the AHI severity levels. Precision, recall (equivalent to sensitivity), and F1-scores ranged from 0.88 to 1.00, indicating high accuracy and reliability. The correlation coefficients are predominantly above 0.9. Similar results were obtained for the secondary endpoints, with no statistically significant differences and high correlation. no safety event occurred.

Conclusion

There is a substantial consistency across the Vlab, and the selected PSG device in capturing various sleep metrics. This consistency is substantiated by robust statistical analyses. In essence, the high level of agreement between these devices sets a strong foundation for their reliability and consistency in sleep diagnostics. The Vlab is deemed substantially equivalent to the reference device NOX in terms of safety, usability, and efficacy.

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0318 Algorithmic Identification of Space-Time Profiles of Slow Oscillations: Dataset Constrains and Use Examples
Authors: Malerba Paola , Snedden Ali , Mednick Sara C
Published: April 2024

Introduction

Given the importance of sleep oscillations in cognition and health, we introduce a data-driven method to analyze slow oscillations (SOs, 0.5-1.5 Hz) as events that differentiate in their space-time co-emergence on the electrode manifold. This approach has identified properties of SO organization that are relevant to function, and that can change in clinical populations. This study formalizes this technique by parameterizing its domain of applicability in terms of dataset properties (e.g., number of participants (N), density of electrodes in the acquisition cap (E)). We define strategies to conduct parameterization of the algorithm and identify the minimum dataset properties required for successful identification of different SO profiles (i.e., Global, Local and Frontal SOs).

Methods

We apply our algorithm to three datasets of healthy young adults: Set1 (N=22, E=58) and Set 2 (N=34, E=24) with nighttime sleep, Set 3 (N= 54, E=24) with daytime sleep (naps). Set 3 showed a count of SOs comparable to Set2. SOs were detected at each channel separately. Role of electrode density was tested by down-sampling Set1 to 24 and 8 channel conditions and Set2 to 8 channels, reflecting standard caps, and assessing the confusion matrix in relation to the full montage. Sample size was tested by randomly selecting subsets of 22 participants from Set2 ten times, and comparing outcomes to original dataset labels. Daytime vs nighttime sleep was evaluated comparing outcomes for Set3 to outcomes of truncating Set1 and Set2 sleep 90 minutes after the first detected SO. Percentages of SO of each type were related to demographics (age, sex) in scatterplots.

Results

We found that identification of SO space-time profiles required at least 22 participants and at least a 24 head-electrode montage, whereas 8 head-electrodes configurations, typical of clinically acquired sleep, were not sufficient. Daytime sleep did not show Global, Local, Frontal SO profiles, while early nighttime sleep did.

Conclusion

This study introduces an open-access code to promote the investigation of SO space-time organization, describing its domain of applicability and exploring the role of demographics in SO profiles. Algorithmic outcomes for daytime sleep suggest a potential role of sleep pressure in shaping SO profiles.

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0319 Using Generative AI as a Tool for Simulating EEG During Nocturnal Polysomnography
Authors: Wickramaratne Sajila , Parekh Ankit , Kam Korey
Published: April 2024

Introduction

Artificial Intelligence based Generative Networks based synthetic data generation has become increasingly popular. Deep Learning Generative models can be trained to model complex data distributions and generate diverse and realistic outputs. We aim to present a generative model that can generate an artificial 30-second epoch of non-REM sleep whose power spectrum is identical to that of a real sleep EEG.

Methods

The data used to train the network was obtained from multiple healthy subjects of the Multi-Ethnic Study of Atherosclerosis (MESA), an NHLBI-sponsored 6-center collaborative longitudinal investigation made available through the National Sleep Research Resource (NSRR). We selected 660 subjects deemed healthy and devoid of any sleep disorders. The EEG was initially recorded at 256 Hz and down sampled to 64 Hz for this study. Only the Fz-Cz channel from the recorded EEG channels was used to train the Generative model. A Deep Learning Generative Adversarial Network was trained using 30s epochs of the NREM Stage 3 EEG data from 660 subjects. Both visual inspection and quantitative metrics were used to evaluate the synthetic signals. The metric to assess Generative Model performance was the relative spectral power (%) in different frequency bands compared to real EEG signals. The averages for the real EEG signals were calculated after taking the average frequency band power across all epochs used for training. The band-power of 100 generated samples, visually deemed good quality, was used to calculate the average for generated EEG signals.

Results

Across Alpha, Delta, Beta, Theta and Sigma bands, there was minimal difference in % band power between synthetic and real EEG signals: Alpha real[0.5±0.14] / Alpha synthetic[2.46±0.52], Beta real[0.1±0.03] / Beta synthetic[0.058±0.03] , Theta real[1.8±0.78] / Theta synthetic[4.18±0.9], Delta real[97.0±1.26] / Delta synthetic[93.2.0±2.25] and Sigma real[0.6±0.04] / Sigma synthetic[0.084±0.01]

Conclusion

The developed system has the potential to be a sleep EEG simulator used as an educational tool to train fellows and technicians across the field of sleep medicine. Future studies are needed to accommodate multichannel EEG signals and other PSG signals (e.g., respiratory, Spo2 etc.) to simulate an entire nocturnal polysomnography.

Support (if any)

This work is supported by NIH R21HL165320, NIH K25HL151912 and NIH R21HL165320
0322 Achieving Deep Sleep in the Magnetic Resonance Environment
Authors: Oshima Hideyuki , West Moon , Nair Vidhya Vijayakrishnan , Kish Brianna , Tong Yunjie , Schwichtenberg A J
Published: April 2024

Introduction

Polysomnography, which includes Electroencephalography (EEG), is a widely used method for categorizing wake/sleep into four distinct states. However, achieving sleep within the magnetic resonance (MR) environment can be challenging given the associated loud sounds and confining space elements. Protocols designed to support sleep in the MR environment are uncommon but are needed to allow for simultaneous fMRI-EEG recordings.

Methods

This study utilized two comparable cohorts to develop a simultaneous fMRI-EEG protocol to maximize deep sleep in the MR environment. In both cohorts sleep patterns were monitored via actigraphy for one week preceding a magnetic resonance imaging (MRI) session. In cohort 1 (n = 8), participants were instructed to wake earlier than usual, resulting in approximately 3 hours of sleep debt before attempting to initiate sleep in the MR environment. During the scan, participants experienced a looped scan sequence (with a relatively higher frequency than the second cohort) for one hour. In cohort 2 (n = 10), a parallel protocol was followed, but the looped scan sequence was extended to 1 hour and 40 minutes and the frequency was lower. Additionally, cohort 2 participants underwent 2-3 nights of sleep onset with MR sound recordings directly before their scheduled MRI session.

Results

The two cohorts had distinct sleep architecture differences; wherein, cohort 2 achieved significantly more deep sleep in the MR environment. Cohort 1 participants had 288 (52%), 69 (12%), 122 (22%), and 30 (5%) minutes of Wake, NREM-1, NREM-2, and NREM-3 respectively. Cohort 2 had 202 (24%), 177 (21%), 150 (18%), and 180 (22%) minutes of Wake, NREM-1, NREM-2, and NREM-3 respectively. Given the complexity of the MR environment, unsure and artifact ‘uncodable’ epochs/minutes were also calculated: cohort 1: 48 (9%) minutes and cohort 2: 121 (15%) minutes.

Conclusion

In both protocols, participants successfully obtained sleep; however, the protocol with sound acclimation and a longer scan sequence resulted in notably higher amounts of deep sleep. Future studies utilizing simultaneous fMRI-EEG measurement would be well advised to follow a similar protocol. As our understanding of brain activity during sleep increases, so will our need to obtain successful simultaneous fMRI-EEG measurements.

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0326 Unobtrusive Detection of Heart-rate-dipping During Sleep Based on Force Sensors in a Smart Bed
Authors: Klibyshev Artur , Shcherbakov Yehor , Rao Megha Rajam , Bansod Suprit , Aduwala Sai Ashrith , Winger Trevor , Barr Shawn , Mushtaq Faisal , Molina Gary Garcia
Published: April 2024

Introduction

Typical heart rate (HR) during sleep exhibits a dipping pattern whose absence is associated with increased risk of all-cause mortality and cardiovascular events. Unobtrusive and longitudinal monitoring of HR patterns may be valuable for early detection of cardiovascular risk. We developed a high-temporal coverage algorithm to estimate instantaneous HR enabling characterization of HR patterns using data from a smart bed.

Methods

Seventeen smart beds were equipped with force sensors under each leg. The force signals acquired at 250 Hz from the sensors located at the bed’s top on the user side were filtered in the 0.5 to 40 Hz band and subsampled to 100 Hz. The data from 226 sleep sessions from 9 participants (5M/3F), mean age 41.7 (SD: 11.3) years-old, were used to train a deep neural network to estimate instantaneous HR every second using data from a 15-second rolling window. The reference HR value was obtained from a Zephyr BioHarness device. The data from 33 sleep sessions from 8 subjects (6M/2F), mean age 41.0 (SD: 11.8) years-old were used to estimate HR, the lowest HR and the latency thereof. The reference HR was obtained from an in-home PSG device (Cerebra). A technician performed the sleep staging and event annotation. The accuracy of estimating instantaneous HR was quantified using a Bland-Altman (BA) analysis at the epoch (30 seconds) level. We defined temporal coverage as the fraction of epochs with HR estimation accuracy ≥ 90%. The HR dipping value was obtained from a smoothed version of the instantaneous HR curve. The HR dipping value and its latency for the ground-truth and the estimated instantaneous HR were compared using BA analysis.

Results

The limits-of-agreement (LoA) for instantaneous HR estimation were [-7.31 to +7.74 beats/min]. The mean coverage across sleep sessions was +92.6 %. The LoA for the minimum HR estimation were [-1.48 to +3.09 beats/min]. The LoA for the minimum HR latency were [-2.28 to +1.79 minute].

Conclusion

Force sensors under a smart bed legs enable accurate and high-temporal coverage HR estimation. This was tested in an HR dipping estimation application resulting in a tight LoA of 4.57 beats/min.

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0332 Descriptive Study of Sleep Patterns and Knowledge Among Department of Juvenile Services Staff
Authors: Bistline Lily , Vermette Erin , Woodward Krista , Carlucci Marianna , Crowley Stephanie , Jackson Dylan , Wolfson Amy
Published: April 2024

Introduction

While research exists on adolescent sleep health in the US juvenile justice system, little is known about the sleep health of the staff that care for them. Poor sleep health, which is associated with negative mental and physical outcomes, may impact the daily interactions between staff and detained youth. The current study explored sleep-wake patterns and sleep health knowledge of DJS staff in Maryland.

Methods

Department of Juvenile Services Staff (N=218) were invited to respond to a survey that queried staff on their knowledge of adolescent sleep needs, job role and schedule, and their own sleep-wake patterns. Descriptive analyses and multivariate analyses of variance (MANCOVA) were conducted to review the staff’s sleep-wake patterns across the existing staff positions and schedules.

Results

Of the DJS staff, 51% served as residential advisors (RAs) who directly supervise youth. Just over half (55%) of staff worked in detention, with the remaining 45% in treatment facilities. Staff reported sleeping 7.24 hours (SD=4.10) on workdays and 8.59 hours (SD=2.69) on non-workdays. Working the night/rotating versus day shifts was significantly associated with reports of greater weekday versus weekend irregularity and oversleep (p=.005), as well as sleepiness during work hours (p’s< .05). Regarding adolescent sleep health knowledge, slightly more than half of the staff (53.9%) were knowledgeable with differences by position type. Specifically, night/rotating RAs and dayshift non-RAs were more likely to agree that sleep disturbances are tied to increased delinquency (41%; 27%) and that adolescent need ranges from 8-10 hours a night (41%; 27%).

Conclusion

Findings demonstrate that DJS staff meet the recommended sleep duration guidelines, however, RA staff experience irregularities in sleep schedule timing and time in bed. The observed staff variability in sleep health knowledge may provide impetus for staff sleep educational programming for staff, especially those who hold non-RA positions. Overall, this study may inform future development and prioritization of sleep and circadian health supports and resources, with the goal of improving sleep conditions for both the detained youth and DJS staff. These sleep health alterations may even have downstream effects in terms of improving relationships between staff and detained youth.

Support (if any)

N/A
0334 Observed Changes in Sleep Duration and Sleep Stage Distribution During Pregnancy Using Wearables
Authors: Heneghan Conor , Lafon Belen , Schneider Logan , Young-Lin Nichole
Published: April 2024

Introduction

Prior studies on the evolution of sleep during pregnancy have either relied on a limited number of observed nights in a sleep laboratory or self-reported sleep values. Consumer wearable devices offer the capability of tracking sleep longitudinally in a large cohort of users, and revealing patterns of interest.

Methods

Users of commercially available wearable devices (heart rate enabled Fitbit devices) were offered to participate in a retrospective study about pregnancy and postpartum. 11470 currently pregnant users, and 13457 new parents (child< 1 year) were consented into the study. By applying strict criteria on wear usage per day (>80%) and throughout pregnancy (>80%), a set of 2540 participants was available for analysis. We considered changes in estimated sleep duration, and the distribution of sleep stages.

Results

Overall time-in-bed (TIB) increased at week 4 of pregnancy and reached a peak around Week 8 with an average increase of 26.7±42.6 minutes. TIB duration then declined at a rate of -1.28 min/week (95% CI:-1.35,-1.22 min/week) towards pre-pregnancy baseline value (~Week 30) and reduced further as the delivery date approached. Total Sleep Time increased from an average of 425.3±43.5 mins to a peak of 447.6±47.6 mins at Week 9 before beginning to decline throughout the rest of pregnancy. The initial increase in sleep duration was mostly due to an increase in light sleep. Deep sleep decreased significantly through pregnancy with an average overall loss of -19.2±13.8 mins by the end of pregnancy. REM sleep also decreased in later pregnancy by -9.0±19.2 mins from baseline. Sleep efficiency declined slightly during pregnancy (median sleep efficiency went from 88.3% to 86.8%).

Conclusion

Consistent with self-report sleep duration and smaller scale laboratory studies, we demonstrated that sleep duration and time-in-bed increases during the first trimester of pregnancy, but then decreases in both quantity and quality as pregnancy progresses.

Support (if any)

This research was funded by Google Inc.
0339 The Perception of the Negative Impact of Workload and Study Load in Canadian University Students
Authors: Duval Félix-Gabriel , Caron Jean-François , Forest Geneviève
Published: April 2024

Introduction

Studies showed that university students are at risk of presenting sleep difficulties. Various factors, including academic workload, and employment status can significantly impact their sleep. The present study aimed to investigate specific factors affecting sleep quality in Canadian university students.

Methods

A total of 1169 Canadian university students completed an online survey. Information about classes, hours devoted to study and work, and the negative impact of their studies and paid work on their sleep was gathered. A multiple regression and a simple linear regression analyses were done to verify the contribution of 1) study load and 2) workload (job) on the perception of a negative impact on sleep.

Results

In total, 64.8% of students were enrolled in at least 4 classes. They spent an average of 23.9 hours per week on their studies. A total of 47.3% reported a negative impact of 7 and more out of 10 (10 being the worst) of their studies on sleep. Analyses on study load show a significant model (F(2, 865)=33.13, p< 0.001) that explained 7% of the variance in the perception of a negative impact of studies on sleep. Results show that being enrolled in more classes (b=.29, t(865)=4.92, p<.001) and devoting more time to their studies (b=.03, t(865)=5.42, p<.001) significantly predict perception of a higher negative impact of studies on sleep. In total, 65.6% of students reported having a job, working an average of 20.1 hours per week. Results show that 56.1% reported a negative impact of 7 or more of their job on sleep. Analyses on workload show a significant model (F(1, 578)=109.91, p<.001) that explained 16% of the variance in the perception of a negative impact of their job on sleep. Results suggest that a higher number of hours worked (b=.10, t(578)=10.48, p<.001) significantly predict perception of a higher negative impact on sleep.

Conclusion

These results raise important issues regarding the study-work-sleep balance of Canadian university students. Many of them have a heavy study load, while many also have a job. Given the importance of sleep for mental health and cognitive performance, these results are of great concern.

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0340 The Influence of Need Satisfaction on Changes in College Students’ Sleep Problems
Authors: Wilcox Isabella , Martindale Jerome , Peltz Jack
Published: April 2024

Introduction

Self Determination Theory (SDT) provides a framework for understanding human motivation by articulating three fundamental needs (i.e., autonomy, belongingness, and competence) that drive/direct behavior and are necessary for our basic functioning and well-being (Deci & Ryan, 1985, 2000). Despite extensive research demonstrating how SDT operates within our daily lives, the association between college students’ sleep and these motivational processes remains relatively opaque. Sleep is critical to college students’ functioning and well-being (Lund et al., 2010), but the few studies linking need satisfaction to sleep and other aspects of functioning have neglected to examine change over time amongst these constructs (Campbell et al., 2015; Campbell et al., 2017). Accordingly, research examining prospective associations between need satisfaction, sleep-related behaviors, and changes in students’ sleep is needed.

Methods

Based on a 2-wave design (baseline and 2-month follow-up), the current study included a sample of 331 participants (86% female) who were assessed through an online survey. The sample’s mean age was 21.3 years (SD=2.4; range 18-34), and 65.9% of participants identified as white, with 18.4% Asian or Pacific Islander, 6.6% Latinx, and 5.7% Black and 3.3% multi-racial or “other.” Students’ mean family income was $108,391 (SD=$62,579), with approximately 22% of students reporting family incomes of $50,000 or less and 16.3% with family incomes greater than $200,000.

Results

Controlling for participants’ gender, family socio-economic status, sleep environment, and sleep hygiene behaviors, regression analyses examined the predictive value of need satisfaction (i.e., autonomy, belongingness, and competency) on residual changes in students’ sleep problems across a 2-month period. After controlling for our covariates, baseline sleep problems (◻ = .54, p < .001), sleep-related competency (◻ = -.13, p < .01), and interpersonal need strain (i.e., lack of belongingness; ◻ = .17, p < .001) predicted residual increases in students’ sleep problems.

Conclusion

Results from the current study suggest that both sleep-related competency and interpersonal needs/belongingness play a key role in changes to college students’ sleep problems. These novel findings highlight the importance of basic need satisfaction in sleep and provide additional evidence for how SDT might be incorporated into sleep research.

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