bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract

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 &lt; 7 hours. Compared to Fitbit measures adolescents generally estimated their sleep period with reasonable accuracy (ICC = 0.182 [0.15, 0.21] p&lt;.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

Abstract

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

Abstract

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.

Support (if any)
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

Abstract

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 &lt;.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

Abstract

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] (&lt; 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

Abstract

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

Abstract

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 (&lt; 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

Abstract

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

Abstract

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.

Support (if any)