bluuebunny/crossref_metadata_2025_split huggingface dataset:
0319 Using Generative AI as a Tool for Simulating EEG During Nocturnal Polysomnography
Authors: Wickramaratne Sajila , Parekh Ankit , Kam Korey 
Published: April 2024

Abstract

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

Abstract

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

Abstract

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

Abstract

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&lt; .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

Abstract

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&lt; 1 year) were consented into the study. By applying strict criteria on wear usage per day (&gt;80%) and throughout pregnancy (&gt;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

Abstract

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&lt; 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&lt;.001) and devoting more time to their studies (b=.03, t(865)=5.42, p&lt;.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&lt;.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&lt;.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.

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

Abstract

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 &lt; .001), sleep-related competency (◻ = -.13, p &lt; .01), and interpersonal need strain (i.e., lack of belongingness; ◻ = .17, p &lt; .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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0342 Eating Habits, Depression, and Poor Sleep: Assessing the Associations in First-Year College Students
Authors: Hirsch Sophie , Peach Hannah , Gaultney Jane 
Published: April 2024

Abstract

Introduction
Transitioning from high school to college often poses significant challenges to students. In many cases, freshmen are now required for the first time to take care of their health habits, including making choices surrounding food and sleep. Diets higher in fats and lower in fruits and vegetables have frequently been associated with an increase in inflammatory biomarkers, which may lead to increased symptoms of depression and poorer sleep (Firth et al., 2019; Hagedorn et al., 2021).

Methods
Given the increased rate of depression (Liu et al., 2022) and sleep disturbance (Luo & Hu, 2022), we conducted a simple mediation analysis using PROCESS macro, Model 4, on first-year college students (N = 73), to assess whether eating habits predicted sleep quality directly, and indirectly via depressive symptoms. We hypothesized that eating habits would directly and indirectly predict sleep quality.

Results
Results indicated that there was no direct relationship between eating habits on sleep quality. However, we found that there was an indirect relationship between eating habits on sleep quality via depression.

Conclusion
Thus, our findings highlight the importance of having a well-balanced diet for different health outcomes. Given that depression and poor sleep are associated with poor school performance due to memory and concentration issues (Dillon & Pizzagalli, 2018; Hershner & Chervin, 2014), promoting healthy food choices for students is necessary to allow for success in college.

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0346 Insomnia in the Initial Months of Emergency Work Is Associated with Future PTSD Symptoms in New Recruit Paramedics
Authors: Wolkow Alexander , Harris Rachael , Nguyen Elle , Meadley Ben , Rajaratnam Shantha M W , Williams Brett , Smith Karen , Bowles Kelly-Ann , Dobbie Megan L , Drummond Sean P A 
Published: April 2024

Abstract

Introduction
New paramedics commencing emergency work and adjusting to shift work can experience an increase in sleep problems. Sleep disturbances have been closely associated with poor mental health in emergency personnel. It is unknown, however, whether poor sleep experienced in the initial months of emergency work represents an early risk factor for future mental health problems in paramedics. The present study examined whether sleep disturbances in paramedics after 6-months of shift and emergency work were associated with mental health symptoms later in their career.

Methods
Sleep disorder and mental health symptoms were examined in new recruit paramedics (n=105) after their first 6- and 12-months of shift and emergency work. At each timepoint, participants completed validated self-report measures of insomnia (Insomnia Severity Index), shift work disorder (SWD; SWD Screening Questionnaire), obstructive sleep apnoea (OSA; Berlin Questionnaire), depression (Patient Health Questionnaire-9), anxiety (Generalised Anxiety Disorder-7), post-traumatic stress disorder (PTSD; PTSD Checklist-5) and trauma exposure (Life Events Checklist-5). Linear regressions examined whether sleep at 6-months (i.e., insomnia, SWD, and OSA) predicted mental health symptoms (i.e., depression, anxiety, and PTSD) at the 12-month follow-up, while controlling for demographics (i.e., age and sex) and mental health symptoms at the 6-month timepoint. Additionally, the regression models examining sleep and PTSD also controlled for trauma exposure at 6-months via the Life Events Checklist-5.

Results
Increased insomnia symptoms at 6-months of emergency work and shift work significantly predicted higher PTSD symptoms at the 12-month timepoint (b=0.09 [0.03,0.15], p=0.01) while controlling for trauma exposure, PTSD symptoms and demographics at 6-months. Insomnia at 6-months was not, however, related to depression (p=0.36) or anxiety (p=0.42) at follow-up, and OSA and SWD risk at 6-months were not related to any of the mental health outcomes at follow-up (all p&gt;0.05).

Conclusion
Our findings highlight insomnia symptoms in the initial months of emergency work as an early risk factor for the development of PTSD later in new paramedics’ careers. Given sleep is a largely modifiable factor, this finding highlights the need to investigate interventions that target insomnia early in paramedics’ careers to reduce the risk of future mental health problems in this high-risk occupation.

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