bluuebunny/crossref_metadata_2025_split huggingface dataset:
0252 Social jetlag, sleep variability, its effect on sleepiness and mediation by sleep debt: an EPISONO study
Authors: Fernandes Guilherme , Vallim Julia , D'Almeida Vânia , Tufik Sergio , Andersen Monica 
Published: May 2023

Abstract

Introduction
Studies indicate the relevance of the timing system as integrating the causes and antecedents of sleepiness. Recently, social jetlag and sleep variability have been highlighted in regard to their relevance to sleep health. While the circadian misalignment represented by those variables have a known effect on sleepiness, it is often confounded by sleep debt. Therefore, we sought to assess the association between social jetlag, sleep variability and sleepiness, with sleep debt as a mediator in a representative population-based study.

Methods
A representative population-based sample of 1037 participants was analyzed. Participants answered the UNIFESP Sleep Questionnaire and Epworth Sleepiness Scale to collect data regarding sleep timing and subjective sleepiness, respectively. Subsequently, mid-sleep time, total sleep time, social jetlag (absolute difference between mid-sleep time on workdays and mid-sleep time on free days), standard deviation of mid-sleep time and sleep debt (difference between total sleep time on workdays and free days) were calculated. Generalized linear models were conducted to assess if sleep variability and social jetlag had an effect on the Epworth Sleepiness Scale score, with BMI as a control variable. A mediation model was run to test if observed significant effects were mediated by sleep debt.

Results
Average sleep variability was 0.69±0.5 hours, and prevalence rates were 23% for &gt;1h and 12% for &gt;2h of social jetlag. There was a statistically significant association between social jetlag and Epworth Sleepiness Scale score, independently of BMI. Moreover, this effect was significantly mediated by sleep debt. No association between sleep variability and sleepiness was observed.

Conclusion
The significant association between social jetlag and sleepiness and its mediation by sleep debt are evidence of their role in sleep health and should be considered as relevant factors in future population-based studies analyzing sleep.

Support (if any)
Our studies are supported by grants from the Associacão Fundo de Incentivo à Pesquisa (AFIP). GLF is supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) – Finance Code 001) and CNPq (#141445/2021-1 to GLF). MLA and ST are Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) fellowship recipients.
0257 Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea
Authors: Manjunath Shashank , Perea Jose , Sathyanarayana Aarti 
Published: May 2023

Abstract

Introduction
Obstructive sleep apnea (OSA) is difficult to diagnose and leads to significant complications, including poor rest and heart disease. Identification of OSA in patients typically requires a polysomnogram, which includes acquisition of electroencephalogram (EEG) signals in order to assist with identification of sleep stages. Recent advancements in signal processing technology, including topological data analysis (TDA), provide a novel new way to analyze EEG signals to assist with identification of OSA before disordered breathing occurs.

Methods
We leverage EEG signals from polysomnogram studies included in the Nationwide Children’s Hospital Sleep DataBank (NCHSDB) dataset for this study. This dataset includes 3,984 pediatric patients, of which we leverage 2,881 containing usable data. We analyze each sleep state (awake before sleeping, awake in between sleep, awake after study, N1, N2, N3, and REM sleep) individually. We first break the data into 30-second chunks, then compute a distance matrix based on the multivariate time series obtained as part of the polysomnogram for the delta, theta, alpha, beta, and gamma bands individually. We then apply TDA to the distance matrices, leading to a set of persistence landscapes for the 0th homology group. We then group persistence landscapes for OSA positive patients and OSA negative patients, and perform a permutation test for each frequency band, leading to a p-value indicating significance.

Results
Based on our permutation test, we find significant (p &lt; 0.05) results in all bands for the N1 (p=0.0 for all bands), N2 (p=0.0 for all bands), N3 (p=0.0 for all bands), and REM sleep (p=0.023 for delta band, p=0.0 for all other bands) cases. In the cases when the patient is awake, we find significant differences in the delta (p=0.022), theta (p=0.007), and beta (p=0.002) bands in the awake before study case, the delta (p=0.003), theta (p=0.003), and beta (p=0.0) bands for the inter-sleep awake case, and no significant differences in the awake after study case.

Conclusion
Our results indicate that a statistically significant difference between OSA positive and OSA negative patients can be identified when patients are awake through topological data analysis.

Support (if any)
0260 Establishment of a polysomnography databank for sleep disorders in Japan
Authors: Kawamura Aoi , Matsui Kentaro , Yoshiike Takuya , Inoue Yuichi , Takeshima Masahiro , Ozone Motohiro , Suzuki Masahiro , Kuriyama Kenichi 
Published: May 2023

Abstract

Introduction
Polysomnography (PSG) has been suggested to be useful as an objective indicator (biomarker) that contributes to the diagnosis and severity evaluation of various sleep disorders. However, its regular use is still limited to the specialized institutions for sleep disorders. Therefore, PSG is essential for clinical diagnosis of only some sleep disorders, and further application of PSG in conjunction with biomarker development of disorders including such as insomnia, and psychiatric disorders with comorbid sleep disorders is expected. The aim of this project is to establish a PSG databank for sleep disorders in collaboration with major PSG laboratories in Japan.

Methods
This project is collaborated with medical and research institutions that conduct PSG on a large scale in Japan. The National Center of Neurology and Psychiatry (NCNP) serves as the core institution. The initial collaborating institution is the Tokyo Medical University, which is in cooperation with the Yoyogi Sleep Disorder Center, the largest PSG facility in the Kanto region. We also collaborate with three other core PSG facilities in the Kanto, Tohoku and Kyushu regions: the Nihon University, the Akita University (in cooperation with the Akita Sleep Clinic), and the Kurume University. This project has been approved by the Ethics Committee of the NCNP.

Results
We have stored about 3,000 PSG data evaluated by the standard of the American Academy of Sleep Medicine clinical practice guideline or the traditional Rechtschaffen & Kales standard with adjunct clinical metadata for the past 9 years at the NCNP, about 17,900 at the Tokyo Medical University, about 1,900 at the Nihon University, about 500 at the Akita University, and about 3,000 at the Kurume University. We are continuously collecting and banking data for enhancing research availability of the PSG databank for sleep disorders.

Conclusion
The collected data will be used for research on the development of objective indices for sleep disorders or neuropsychiatric disorders, for which objective indices are lacking in diagnosis and pathological evaluation.

Support (if any)
This work was supported by the Intramural Research Grant (#2-1) for Neurological and Psychiatric Disorders of NCNP.
0261 Sleep Disturbance Reliably Predicted by Pain Sensitivity in Sciatic Nerve Crush Injury Model
Authors: Ho Anh , Kim Tae , Lee Sungjun , Drew Victor , Kang Jiseung , Jung Jieun 
Published: May 2023

Abstract

Introduction
A bi-directional relationship between sleep and neuropathic pain is of interest because of its impact on human health. Numerous studies in humans and animals have examined the association between sleep and neuropathic pain. However, animal studies showed inconsistent results compared to human data. Therefore, validation of an animal model for sleep-pain association research is needed. We examined the animal models to identify a best-fit model predicting sleep disturbances with the level of neuropathic pain.

Methods
Adult male C57BL/6J mice (n=7 per group) were implanted with electrodes for electroencephalogram (EEG) and electromyogram. After two weeks of recovery, we obtained baseline EEG data for 24 hours and evaluated the paw withdrawal threshold, a measure of pain sensitivity, by the von Frey test. Afterward, sciatic nerve crush injury (SCI) and common peroneal nerve ligation (CPL) were conducted to make the two different neuropathic pain models. Post-surgery sleep was recorded continuously for 24 hours, followed by von Frey tests. EEG recording during the light period and von Frey tests were repeated on post-surgery days 5, 10, and 15.

Results
Both SCI and CPL models showed lower paw withdrawal thresholds after the peripheral nerve injury. NREM sleep duration on post-surgery day 1 was reduced significantly compared to baseline in both SCI and CPL, but only SCI showed decreased REM sleep. There was a significant positive correlation between paw withdrawal threshold and NREM sleep duration in SCI (P&lt; 0.0001) but not CPL. Also, wake alpha and theta EEG powers were correlated with pain threshold.

Conclusion
We confirmed that both models of neuropathic pain disturbed sleep patterns. Nevertheless, sleep disturbances were reliably predicted by the pain sensitivity in the SCI model. Hence, we suggest SCI as a reliable pain model for studying the mechanism of sleep-pain association.

Support (if any)
0262 Comparing Wrist Actigraphy to a Novel Wearable (Actigpatch): Nonparametric Activity Estimation
Authors: Markowitz Alexandros , Barker David H , Saletin Jared , Gredvig-Ardito Caroline , McGeary John , Carskadon Mary 
Published: May 2023

Abstract

Introduction
Wrist actigraphy is a standard for monitoring sleep in the field; however, data quality is reduced if participants remove the device. The Actigpatch is a novel, adhesive water-resistant wearable that we have previously demonstrated as comparable to wrist actigraphy for traditional sleep-wake estimation. Here we compare assessment of nonparametric activity indexes.

Methods
While following fixed schedules, 39 participants simultaneously wore the Micro Motionlogger actigraph (Ambulatory Monitoring Inc., Ardley, NY) on their non-dominant wrist and the Actigpatch (Circadian Positioning Systems, Newport, RI) over the triceps of their dominant arm. Our analyses included 35 participants (21F; 32.9±13.2yrs) who contributed ≥four nights of data (range: 4-14 [mean: 10] nights). After matching devices’ tri-axial actimetry in one-minute epochs, we derived key non-parametric parameters of diurnal activity and calculated intraclass correlations to measure agreement. The non-parametric parameters include interdaily stability (IS), intradaily variability (IV), timing of the five hours of lowest activity (L5onset) and ten hours of highest activity (M10onset), and overall relative amplitude (RA).

Results
We observed agreement ranging from good for IS (ICC=0.77 [95%CI=0.59;0.88]; [all mean differences are patch-watch] mean difference=-0.21) to poor for IV (ICC=0.43 [0.12;0.67]; mean difference=0.29). ICC showed good agreement for M10onset (ICC=0.82 [0.67; 0.90]; mean difference=-37min) and excellent agreement for L5onset (ICC=0.91 [0.82;0.95]; mean difference=-2min). Finally, we identified good agreement when estimating the activity relative amplitude (RA ICC=0.86 [0.75; 0.93]; mean difference=0.03). An example of excellent agreement was manifested in the close estimation of L5onset (patch mean=1:04am, SD=70min; watch mean=1:06am, SD=68min).

Conclusion
Adding to our prior evidence that these two devices offer similar sleep-wake estimation using traditional algorithms, the present data indicate the Actigpatch offers good agreement to the Motionlogger for nonparametric analyses of IS, activity timing, and RA. Agreement was not as good for IV, with the Actigpatch showing more intradaily variability than the watch, perhaps due to the triceps placement. Because the Actigpatch is unobtrusive, water-resistant, and can be worn continuously for three weeks, it has potential benefits for studies of individuals who struggle with adherence to wearing wrist worn devices.

Support (if any)
R01AA025593, P20GM139743. Actigpatches provided by Circadian Positioning Systems.
0265 Validation Framework for Sleep Stage Scoring in Wearable Sleep Trackers and Monitors with Polysomnography Ground Truth
Authors: Le Trung 
Published: May 2023

Abstract

Introduction
The rapid growth of point-of-care polysomnographic alternatives has necessitated standardized evaluation and validation frameworks. The current average across participant validation methods may overestimate the agreement between wearable sleep tracker devices and polysomnography (PSG) systems because of the high base rate of sleep during the night and the interindividual difference across the sampling population.

Methods
This study proposes an evaluation framework to assess the aggregating differences of the sleep architecture features and the chronologically epoch-by-epoch mismatch of the wearable sleep tracker devices and the PSG ground truth. An AASM-based sleep stage categorizing method was proposed to standardize the sleep stages scored by different types of wearable trackers. Sleep features and sleep stage architecture were extracted from the PSG and the wearable device’s hypnograms. Therefrom, a localized quantifier index was developed to characterize the local mismatch of sleep scoring.

Results
We evaluated different commonly used wearable sleep tracking devices with the data collected from 22 different subjects over 30 nights of 8-h sleeping. The proposed localization quantifiers can characterize the chronologically localized mismatches over sleeping time. The proposed framework not only characterizes the difference in the sleep stage distribution but also quantifies the chronologically localized mismatches over the sleeping time. Three major components of the framework consist of AASM-based sleep stage classification, sleep staging distribution analysis, and localized comparison analysis. The outperformance of the proposed method over existing evaluation methods was reported.

Conclusion
The proposed research extends our earlier work on evaluating commercial sleep trackers compared to PSG. The proposed evaluation method can be utilized to improve the sensor design and scoring algorithm.

Support (if any)
Nguyen, Quyen NT, et al. "Validation Framework for Sleep Stage Scoring in Wearable Sleep Trackers and Monitors with Polysomnography Ground Truth." Clocks & sleep 3.2 (2021): 274-288
0269 No More Actiwatches: Can Apple Watches be a More Scalable Alternative?
Authors: Caputo Dominick , Cheng Philip , Walch Olivia , Bayoneto Alec , Hannay Kevin , Moreno Jennette , Iqal Justin , Drake Christopher 
Published: May 2023

Abstract

Introduction
The Philips Respironics Actiwatch has become a gold standard for actigraphy data collection. With the announcement of their discontinuation, there has been increased momentum to identify an alternative, particularly with consumer-based devices. One promising solution is the Apple Watch because it allows user access to raw accelerometer data, thus eliminating the long-standing problem of the “black box algorithm” with wearable technology. This study compared the activity counts derived from Apple Watch data with that from the Actiwatch.

Methods
Adults wore an Actiwatch and Apple Watch on the same non-dominant wrist for 7 to 14 days (mean = 9). Accelerometer data were recorded and activity counts were derived from accelerometer data (algorithm for Apple Watch from Lindert et al, 2013). Daily sleep diaries were also completed. Actigraphy activity counts were binned into 2, 5, 10, 30, and 60 minute bins to examine concordance by bin sizes. Agreement between Apple Watch and Actiwatch were quantified with Lin’s Concordance Correlation Coefficients (CCC) across bins. Usability was assessed utilizing an exit survey.

Results
In increasing order of bins, the CCCs were: .63 (2 mins.), .70 (5 mins.), .78 (10 mins.), .90 (30 mins.), and .92 (60 mins.). Agreement was substantial for bins of 5 minutes or greater with 30 and 60 minutes showing the strongest agreement. The y-intercepts for all bins were positive indicating that Apple Watches were more likely to detect activity counts compared to Actiwatches. In terms of usability, a large majority (88%-100%) of participants favored the Apple Watch for comfort, convenience, preference to wear in public, and preference to wear again. The only dimension where there was not a clear preference was ease of use, with 56% indicating a preference for the Apple Watch.

Conclusion
This is the first study supporting Apple Watch as a potentially feasible alternative to an Actiwatch. Given that the Apple Watch’s accelerometer appears to be more sensitive, use of a different algorithm for determining activity counts from raw accelerometer data may improve the concordance at smaller bins. Future research should explore the effectiveness of the Apple Watch for sleep/wake detection in clinical samples.

Support (if any)
0270 Obstructive Sleep Apnea detection using ECG morphology and Machine Learning
Authors: Sayeed Fahad , Brooks Justin , Banerjee Nilanjan 
Published: May 2023

Abstract

Introduction
Obstructive Sleep Apnea (OSA) is characterized by reduction in airflow. Hypopnea is a smaller reduction in airflow compared to apnea but also accompanied by drop in oxygen saturation leading to sympathetic activation. Frequent OSA events are correlated with incidence of cardiovascular diseases. Measuring changes in airflow requires overnight polysomnography that is expensive. Electrocardiogram (ECG) is available through wearable devices at home and therefore, detecting OSA events using ECG can make OSA diagnostics more accessible. This paper studies the use of ECG morphology and an ensemble machine learning algorithm to detect OSA events. The algorithm designed is light weight to ensure that it can be implemented on an embedded processor.

Methods
The data from the Apnea, Bariatric surgery, and CPAP (ABC) study provided by the National Sleep Research Resource is used for the analysis. Twenty-six subjects diagnosed with severe OSA are considered with single night polysomnography before treatment. Since OSA events have a minimum 10s duration, 10s non-overlapping windows with OSA labels from a technician scoring is used. Features are extracted from the PQRST complex and majority voting across multiple algorithms is used to select the top 7 features (SDNN, RMSSD, P-P interval, P-duration, P-R interval, T-duration and T-P interval) with highest explanatory power. We train a random forest classifier using leave-one-out methodology where 25 subjects are used for training and the 26th subject is used for testing (all permutations are used). We use sensitivity, precision and F1 score for evaluation.

Results
The algorithm detects the precise occurrence (onset and offset) as well as the total number of events during the night. The precision, sensitivity and F1 scores are 70%, 96% and 80% respectively. The prediction leans towards higher occurrence of OSA events as the subjects suffer from severe OSA. We uncover 32% of false positives are events that are accompanied by significant SPO2 desaturation pointing to the inaccuracy of manual scoring. Assuming these false positives are correct predictions the precision increases to 79%.

Conclusion
The classification of OSA events using ECG features and machine learning demonstrates the feasibility of using wearables to measure OSA in a home setting.

Support (if any)
0276 Detecting Apnea Hypopnea Index for Classified the Severity of Obstructive Sleep Apnea using PPG signals
Authors: Chiu Amy , Shiao Yao , Liu Yu Ting , Lin Chia Mo , Chen Chia Chi 
Published: May 2023

Abstract

Introduction
A polysomnography or home sleep apnea study provides multiple pieces of information to diagnose obstructive sleep apnea (OSA), but the tests are costly with limited access. The study aims to use an automated AHI model with only PPG signals and can be applied to a wearable device.

Methods
We have included patients with different OSA severity to build an algorithm detecting ODI based on the scoring criteria with varying sizes of windows ranging from 10 to 60 seconds. For patients without ODI events, the automated CPC for detecting low-frequency oscillation is included to support the automated AHI model.

Results
The automated ODI and the combination of automated CPC are highly correlated with the AHI. When a CPC is detected without the ODI, the low-frequency coupling can assist in detecting AHI. The accuracy of the automated AHI is 86% compared to the actual AHI, with the sensitivity, specificity and precision at 92%, 73% and 89%, respectively.

Conclusion
The automated AHI algorithm with PPG signals as input can have a high sensitivity and accuracy in screening patients with OSA (AHI≥5), which can considerably be implied in a PPG wearable device.

Support (if any)
0278 Patient Satisfaction with a Novel Sleep Telehealth Platform in the US Military
Authors: Adornetti Julianna , Huang Kathleen , Venezia Janet , Wade Christine , Jones Rachell , Collen Jacob , Capaldi Vincent , Williams Scott , Assefa Samson , Albrecht Jennifer , Wickwire Emerson 
Published: May 2023

Abstract

Introduction
Despite a high prevalence of sleep problems, there is a gross shortage of sleep clinicians within the military health system. To increase access to care, a novel telehealth platform was designed to empower patients and clinicians to make evidence-based sleep treatment decisions. Here, we present initial results from the implementation of this novel sleep telehealth platform in two military treatment facilities (MTFs).

Methods
Participants (N=305) with sleep complaints were recruited remotely and/or in-person from two MTFs. Participants were predominately men (53.4%), who self-identified as White (55.4%) or Black (24.1%) race and were retired or active-duty military servicemembers (69%). Average age was 45.9 (SD=13.3) years. Participants completed a baseline assessment including standardized research questionnaires via a mobile application (WellTap®). During a 10-day remote monitoring assessment, participants completed daily sleep diaries and twice-daily mood/daytime function surveys, while wearing a commercial-off-the-shelf device (Fitbit®). A personalized sleep report including evidence-based treatment recommendations was provided. Participants completed a satisfaction survey regarding the remote monitoring assessment.

Results
The most commonly reported sleep problems were difficulty staying asleep (65%), insufficient sleep (61%), and snoring (40%). Most participants described their sleep quality as “a little good” (51%) or “not at all good” (27%). Thirty-nine percent of participants reported moderate to severe insomnia complaints, 38% reported moderate to severe excessive daytime sleepiness, and 22% reported moderate to severe depressive symptoms. In terms of satisfaction, participants reported the sleep assessments were “easy” to complete (92%) and would consider sleep telehealth approaches in the future (95%). The vast majority of participants preferred to continue with sleep telehealth and preferred to combine a mobile app and remote monitoring with either virtual or face-to-face provider support.

Conclusion
Patients at two military treatment hospitals reported high levels of satisfaction with a novel 10-day intensive remote monitoring assessment including a mobile health assessment and wrist-wearable device. Future studies should evaluate the clinical and economic impact of sleep telehealth and remote monitoring in the U.S. military.

Support (if any)
Department of Defense (W81XWH1990006) via the Medical Technology Enterprise Consortium) to the University of Maryland, Baltimore (PI: EMW).