bluuebunny/crossref_metadata_2025_split huggingface dataset:
0305 Can We Rely on Wearable Sleep-Tracker Devices for Fatigue Management?
Authors: Reifman Jaques , Priezjev Nikolei , Vital-Lopez Francisco 
Published: April 2024

Abstract

Introduction
The importance of sleep in daily life and advancements in our ability to measure bio-signals have led to an unprecedented growth in the use of commercial wearable sleep-tracker devices. However, sleep parameters estimated from these tracker devices often differ from the gold-standard polysomnography (PSG). It is unclear to what extent we can tolerate these errors within the context of a particular clinical or operational application. Here, we sought to develop a method to quantitatively determine whether a sleep tracker yields acceptable sleep-parameter estimates for assessing alertness impairment.

Methods
Using literature data, we characterized sleep-measurement errors of 18 unique sleep-tracker devices with respect to PSG. Then, using predictions based on the well-validated Unified Model of Performance, we compared the temporal variation of alertness for simulations with and without added PSG-device sleep-measurement errors, for nominal schedules of 5, 8, or 9 hours of sleep/night, or an irregular sleep schedule each night, for 30 consecutive days. Finally, we deemed a device error acceptable when the predicted differences were smaller than the within-subject variability of 30 ms.

Results
On average, the 18 sleep-tracker devices overestimated sleep duration by 19 (standard deviation = 44) minutes. Using these errors for 30 consecutive days, we found that, regardless of sleep schedule, in nearly 80% of the time the resulting predicted alertness differences were smaller than 30 ms, suggesting that most devices yield acceptable sleep-measurement errors. We also established the capability to estimate the extent to which a specific sleep-tracker device meets this acceptance criterion.

Conclusion
We provide a method to quantitatively determine whether a sleep-tracker device produces sleep measurements that are operationally acceptable for fatigue management.

Support (if any)
This work was sponsored by the Military Operational Medicine Program Area Directorate of the U.S. Army Medical Research and Development Command (USAMRDC), Fort Detrick, MD. The Henry M. Jackson Foundation was supported by the USAMRDC under Contract No. W81XWH20C0031.
0306 Improving Accessibility and Minimizing Errors: Reliable Sleep Stage Classification Using Wearable Sensors
Authors: Schabus Manuel , Baron Sebastian , Topalidis Pavlos , Hinterberger Alexandra , Eigl Esther-Sevil , Heib Dominik 
Published: April 2024

Abstract

Introduction
The widespread use of wearable devices has led to the emerging trend to quantify sleep. However, concerns arise due to the potential inaccuracies in these devices, which even can result in adverse daytime consequences. Here, we present an automatized, low-cost, deep learning alternative to PSG sleep staging that provides a reliable epoch-by-epoch classification in four classes (Wake, Light [N1 + N2], Deep, REM) and solely relies on inter-beat-interval (IBI) HRV data.

Methods
We trained a multi-resolution convolutional neural network (MCNN) on the IBIs of 8898 manually sleep-staged full-night (PSG) recordings and tested the MCNN for classification accuracy using two affordable consumer wearables: an optical heart rate sensor using photoplethysmography (PPG-VS) and an ECG breast belt (H10). Our most recent study tests the model on 136 self-reported poor sleepers equipped with PSG and these wearables.

Results
Results indicate promising outcomes with high epoch-by-epoch accuracy for ECG (accuracy: 86.3%, κ = 0.79), the H10 ECG (84.4%, κ = 0.76), and the PPG-VS wearable (84.2%, κ = 0.75). The HRV-based model exhibits substantial correlations and agreement with PSG on key sleep parameters, e.g., for Sleep Onset Latency (ρ = 0.82, p &lt; 0.001), Sleep Efficiency (ρ = 0.84, p &lt; 0.001), Total Sleep Time (ρ = 0.94, p &lt; 0.001) and REM sleep (ρ = 0.78, p &lt; 0.001). Notably, the model accurately classifies sleep also among users on heart-affecting or psychoactive medication; a critical aspect for potential clinical use. A first RCTs using this algorithm in our App “Nukkuaa” tested effects of i) daily objective sleep feedback using such sensors in combination with ii) tailored sleep training for insomnia patients using digital cognitive behavioural therapy (dCBT-I) and revealed subjective as well as objective improvements.

Conclusion
The present validation underscores the potential of automatic sleep stage classification algorithms based on cost-effective wearables and paves the way for scientific and clinical applications which strive for continuous and reliable sleep measurements including epoch-by-epoch sleep stage information as known from traditional PSG.

Support (if any)
The study was supported by the county of Salzburg (20102-F2002176-FPR) and the FWF/Austrian Science Fund (W 1233-B) for P. Topalidis.
0308 Non-Contact Sleep Monitoring with a Depth Camera: Morphology Similarity Between a Touchless Flow Signal and RIPflow
Authors: Addison Paul , Antunes Andre , Montgomery Dean , Smit Philip , Borg Ulf , Brewer Lara , Farney Robert , Sundar Krishna 
Published: April 2024

Abstract

Introduction
We have developed a non-intrusive sleep monitoring technology based on data from a commercially available depth sensing camera [Intel RealSenseTM D415, Intel, Santa Clara, CA]. This non-contact, or touchless, technology allows continuous respiratory monitoring without attaching probes to the patient. We have noticed a strikingly similar morphology between the touchless flow signal, NCMflow, derived from the depth camera and the RIPflow signal derived from the chest and abdomen respiratory inductance plethysmography (RIP) bands. Here we report on a preliminary quantitative assessment of this morphological similarity between the two signals.

Methods
For this proof-of-principle study, we analyzed a patient data set acquired continuously during a 9-hour sleep study at the Sleep | Wake Center at the University of Utah, where informed consent was given. Depth data was acquired with the depth camera placed approximately 1.1m above the torso of the patient. A patient with severe sleep apnea (AHI of 74/hr) was selected to compare NCMflow and RIPflow, and we examined both the concordance and the Pearson coefficient, which are measures of the mutual trending and linear correlation between the two signals respectively. The concordance was examined in two distinct timescales (1 and 2 seconds), which allowed for intra-breath morphology analysis, using a data exclusion zone of 10% to account for background signal noise. Both concordance and Pearson coefficient of linear correlation were calculated over the full 9 hours of the sleep study (including sleep and wake periods).

Results
The concordance for timescales of 1 and 2 seconds were 0.93 and 0.95 respectively, (where complete concordance would equal 1.00 and correspond to perfect co-trending). The corresponding Pearson R value was 0.90, (out of a maximum achievable value of 1.00 corresponding to perfect linear correlation between the signals).

Conclusion
The high values of the concordances and R confirm the excellent match we observe visually between the touchless flow signal, NCMflow, and the RIPflow signal. Our findings strongly support the potential for non-contact continuous monitoring of respiratory disturbances during sleep based on a depth camera system. Future work will aim to determine the viability of developing a touchless sleep scoring methodology.

Support (if any)
The work was funded by Medtronic.
0313 Analysis of Objective and Subjective Sleep Metrics and Smartphone Usage Patterns
Authors: Heneghan Conor , McDuff Daniel , Winbush Ari , Allen Nicholas , Hernandez John , Jiang Allen , Barakat Andrew , Schneider Logan , Nelson Benjamin , Yetton Ben 
Published: April 2024

Abstract

Introduction
The Digital Wellbeing Study is an IRB approved joint study between the University of Oregon and Google to investigate how smartphone usage interacts with objective and subjective parameters of well-being such as sleep, exercise and stress. The study recruited a demographically diverse population who each wore a smartwatch and installed a smartphone app linked to the study. Participants completed demographic and health questionnaires including the PROMIS Sleep Disturbance (SD) Short Form. Aims of the study included (a) whether objective sleep duration was correlated with smartphone use, and (b) whether smartphone usage could predict the subjective self-reported sleep instrument.

Methods
There was sufficient data from 7,499 users to conduct a population modeling analysis. An Ordinary Least Squares linear model was used as a predictor of each subject’s average total sleep time (TST) and their SD t-score. The inputs to the model included demographics, and population z-scored activity measures (steps, sedentary time, time driving, time at work, home and other locations, phone screen time, frequency of phone unlocks) over seven days prior to the survey.

Results
The activity measures and baseline demographics could only explain a small amount of the overall variance in TST and SD (R^2=0.04 for TST and R^2=0.05 for SD). Phone screen time was a statistically significant predictor of both TST (-8.19 mins, p&lt; 0.001) and self-reported sleep disruption (0.611 t-score units, p&lt; 0.001). The number of phone unlocks was a predictor of variability in TST (-3.33 mins, p&lt; 0.001) suggesting that longer session times are correlated with greater TST variability. The effects are minimal (e.g., a subject who has one standard deviation greater phone screen time than average would be predicted to only see a 2% reduction in TST, and a 0.6% increase in perceived sleep disturbance). Time driving and step count were also minor predictors of SD and TST.

Conclusion
At a population level, average activity measures from wearables and smartphones such as steps, smartphone usage time, sedentary activity etc. are limited predictors of objective sleep metrics such as Total Sleep Time, and subjective sleep metrics such as the PROMIS Sleep Disturbance t-score.

Support (if any)
This research was funded by Google Inc.
0314 Design of a Continuous Melatonin Monitoring System: A Needs Assessment for Direct and Indirect Users
Authors: McCormack Cassandra , Garrison Michelle , Caldwell Barrett 
Published: April 2024

Abstract

Introduction
Circadian misalignment can interact bidirectionally with existing chronic conditions, especially those affecting the physical-cognitive boundary, in a manner causing progressive degradation of the chronic condition and circadian health. Current techniques for detecting circadian misalignment involve salivary, serum, or urinary melatonin testing or applying machine learning to accelerometry and heart rate data from wearable devices, and have considerable limitations affecting accessibility, generalizability, and real-time intervention. A continuous, patient-facing melatonin monitoring device (CMM) offers an alternative detection method while also supporting patients in performing real-time self-care tasks to improve their circadian alignment, sleep health, and wider health outcomes. Systematic design processes emphasize the importance of identifying end-user needs to derive specific design system requirements.

Methods
Three relevant frameworks were to support a process for stakeholder needs assessment to improve circadian / sleep management. Diffusion of innovations addresses factors that impact the adoption of an idea or product through social networks. Health literacy framework references patient knowledge and beliefs that influence health behaviors and outcomes. The chronic care model considers community and health system factors affecting interactions between patients and provider teams. These frameworks were adapted to a sleep health context and used to develop the NAP-CART (Needs Assessment Protocol for a Circadian Assessment and Realignment Tool).

Results
The NAP-CART involves performing semi-structured interviews with direct users (patients) and indirect users (sleep researchers and clinicians), with 15-20 subjects of each. Patient interviews using interface drafts will focus on health literacy specific to circadian alignment and melatonin, user interaction preferences for both short-term and sustained use, including broader issues of health management. Inclusion of indirect users will inform device use for research or clinical settings, but also provider interactions with patients specific to melatonin data, as patients are unlikely to have had previous interactions with melatonin data. Indirect user interviews will also consider the prospective role of CMM in research, clinical care, and decision-making support.

Conclusion
User-centered needs assessments for NAP-CART go beyond technical design of the CMM physical sensor. Incorporating end-user needs, previous experiences, and preferences into the systematic development of specific, measurable design requirements facilitates both initial adoption and sustained use for patients and clinicians.

Support (if any)
0315 Exploring AHI Variability Across Nights: Insights from Wearable Technology in Patients with No or Mild OSA
Authors: Link Brittany , Tsai Chih-Wei , Palomo Juan , Leung Lydia , Cheung Cynthia , Chiang Ambrose 
Published: April 2024

Abstract

Introduction
Night-to-night variability (NtNV) of obstructive sleep apnea (OSA) severity is a widely recognized phenomenon in sleep testing. Studies suggested that 20% of patients with mild-to-moderate OSA may be misdiagnosed or misclassified on the first night. Multiple-night assessments utilizing wearables may present a convenient approach to enhance the reliability of OSA estimation. The Belun Sleep System (BLS-100, a.k.a., Belun Ring) is an FDA-cleared, deep-learning-powered home sleep apnea testing system. This analysis aims to assess the NtNV of Belun apnea-hypopnea index (bAHI, events/h) in subjects with no or mild OSA.

Methods
Participants were recruited from a dental school orthodontic clinic for multiple-night Belun Ring (BR) testing. The bAHI was derived for each night, and a reference bAHI was computed based on the mean from all nights. Linear mixed-effects (LME) models were employed to evaluate differences between night-specific bAHI (1st, 2nd, 3rd night, etc) and the reference bAHI.

Results
Thirty-six patients underwent BR testing, with 30 completing ≥3 nights with ≥60 mins/night. Analysis was conducted on 28 subjects with no or mild OSA: age 31.7; 73% female; BMI 24.6; average 6.5 nights (3-10); mean total sleep time 340.0 mins; average AHI 6.2; 46% no OSA and 54% mild OSA. No significant differences were observed between the first night bAHI and the reference bAHI (6.8 vs. 6.2, P=0.362). This trend held true for subgroups with no OSA (3.9 vs. 3.6, P=0.712) and mild OSA (9.3 vs. 8.5, P=0.716). The first night study misclassified 4 (14%) subjects, including 3 (10%) non-OSAs as mild OSA and 1 (4%) mild as moderate OSA (κ=0.72, P&lt; 0.001). While the number of assessment nights was non-significant in NtNV (LME, P=0.618), a drop in misclassification from 14% to 4% after 4-night (κ=0.92, P&lt; 0.001) suggested that a 4-night evaluation may offer a more dependable bAHI assessment.

Conclusion
This research marks the first exploration into the AHI NtNV utilizing wearables in subjects with no or mild OSA. No significant bAHI differences were observed across the nights. A 4-night assessment may provide a more reliable OSA estimation within this particular population. Further investigation into AHI NtNV utilizing BR in moderate-to-severe OSA is warranted.

Support (if any)
0320 Foundational Transformers with Linear Probing for Sleep Stage Classification Using Time Series Sleep Study Data
Authors: Fox Benjamin , Wickramaratne Sajila , Parekh Ankit , Nadkarni Girish 
Published: April 2024

Abstract

Introduction
Sleep disorders and deprivation disrupt people's daily activities, mental health, and longevity and are related to widespread conditions. Currently, sleep disorders are diagnosed via polysomnography (PSG), where electrophysiological data is collected and manually annotated by a clinician. State of the art machine learning (ML) models, such as the transformer, are particularly well-suited for modeling timeseries PSG data. Specifically, self-supervised models with linear probing could assist with any relevant sleep predictive task including automating sleep stage classification, which would save clinicians time, reduce variability in manual scoring, and help scale to treat more people.

Methods
Using a self-supervised learning approach and the transformer architecture, we trained a self-supervised ML model that inputs seven PSG channels of length three hours including electroencephalogram, electrooculogram, electromyography, electrocardiography, oxygen saturation, and thoracic and abdomen respiratory rate using the Sleep Heart Health Study database (1995-1998). The model architecture uses the transformer’s attention mechanism to learn long range dependencies between intervals of sleep and a convolutional layer to learn relationships among channels. The model learns representations of PSG data through masked reconstruction with a mean squared error loss function. The representations are used as input into a deep neural network that is trained via linear probing (without adjusting the weights of the transformer model) to classify sleep stages.

Results
5,794 sleep studies from the Sleep Heart Health Study with at least three hours of relevant PSG sleep channel and hypnogram data are included in training the self-supervised and linear probing models. Area under the receiver operator characters curve for sleep stage classification are 0.960 [0.960-0.961], 0.848 [0.846-0.849], 0.906 [0.906-0.907], 0.968 [0.967-0.968], and 0.931 [0.930-0.932] for wake, stage 1, stage 2, stage 3, and REM, respectively. Hyperparameter tuning, class weighting, and dataset cleaning will be performed to increase classification results.

Conclusion
A self-supervised training approach using the transformer architecture with linear probing was utilized to learn multichannel PSG data representations. These representations were used as input into a downstream model to classify sleep stages accurately. Future work should be done to examine the capabilities of the self-supervised model representations for other predictive sleep tasks.

Support (if any)
NIH K25HL151912, NIH R01HL171813, NIH R21HL165320
0321 Luna: Merging Open-source Tools, Data and Models for Sleep Signal Analyses
Authors: Purcell Shaun M · 
Published: April 2024

Abstract

Introduction
Data-sharing is increasingly recognized as a critical driver of discovery and reproducibility in modern biomedical science. To realize the promise of data-sharing, important corollaries are the need for open tools, standards and models to work with and make sense of those data. Here we outline a new software framework (Luna), which is tightly coupled to data in the National Sleep Research Resource (NSRR) and supports predictive models based on these and other datasets.

Methods
Luna is well-documented, multi-platform open-source software package that supports a variety of analyses of sleep macro- and micro-architecture and is being integrated into the NHLBI BioData Catalyst ecosystem. Web-based Moonlight & Moonbeam components support the direct access, interactive viewing and analysis of NSRR data. We are also developing modular and extensible model-based prediction capabilities in Luna, to predict outcomes such as biological age directly from raw sleep (EEG) signals.

Results
We will present specific results from three exemplar applications of Luna: 1) a sleep stager and biological/brain age prediction model tailored to pediatric contexts, trained on over 5000 individuals, 2) risk models for schizophrenia from high-density sleep EEG data, and 3) sleep-based biomarkers of cognitive aging in older adults. As well demonstrating the performance of these models, we will discuss challenges to validity and interpretation more generally.

Conclusion
The power of open sleep data is greatly augmented by robust tools, standards and models to facilitate working with those data. Greater community-wide sharing and transparency of tools and methods is an important step to greater reproducibility and rigor in sleep science.

Support (if any)
NIH/NIA 1R01AG070867; NIH/NHLBI 5R01HL146339; NIH/NHLBI 75N92019C00011.
0323 Is Snoring Associated with Lower Sleep Quality? If Yes, Does the Association Depend on Sleep Apnea?
Authors: Molina Gary Garcia , Chellamuthu Vidhya , Gorski Brandon , Mills Rajasi , McGhee Leah , DeFranco Susan , Aloia Mark 
Published: April 2024

Abstract

Introduction
Snoring, as a common manifestation of sleep apnea, has often been used as a surrogate parameter for apnea. Significant associations were found between snoring and daytime sleepiness and cardiovascular conditions. The goal of this research is to use objective sleep data collected by the Sleep Number platform and survey responses to examine the relationship between snoring and sleep quality depending on the presence of treated or untreated apnea.

Methods
An IRB-approved survey, which included demographics and questions about sleep and its associated disorders, was presented to a cohort of Sleep Number (SN) customers during the period between June 12 to 26, 2023. Objective data collected by the SN platform included sleep duration, restful sleep, sleep latency, mean respiration rate, mean heart rate, mean heart rate variability, and sleep quality. The latter considers a demographic dependent aggregation of sleep duration, restful sleep and cardiorespiratory activity. Data were collected May 1st - June 30-2023, and were augmented by survey responses. Four groups were defined for analysis HNS: non-snorers reporting no sleep disorder, HES: snorers reporting no sleep disorders, TAS: snorers with treated apnea, and UAS: snorers with untreated apnea. Analysis of variance (ANOVA) was performed to identify any significant objective data differences between the groups. A second analysis focused on identifying the specific metric showing significant differences.

Results
The demographic responder composition per group was HNS 2454 (1717F/734M) individuals aged 53.9 (SD: 15.0) years-old, HES: 4702 (2626F/2069M) individuals aged 55.1 (SD: 13.5) years-old, TAS: 1784 (679F/1103M) individuals aged 59.7 (SD: 12.0) years-old, and UAS: 134 (59F/75M) individuals aged 59.4 (SD: 10.9) years-old. The ANOVA revealed significant objective data differences between groups. Only sleep quality showed significant group differences. Quality decreased significantly with respect to the HNS reference. Healthy snorers had a 0.96% lower quality, snorers with treated apnea had 1.8% lower quality, and snorers with untreated apnea had 3.9% lower quality.

Conclusion
Compared to healthy non-snorers, sleep quality quantification by a smart bed suggests a gradual decrease for healthy snorers, snorers with treated apnea, and snorers with untreated apnea.

Support (if any)