bluuebunny/crossref_metadata_2025_split huggingface dataset:
0279 Intermittent Analysis of Obstructive Sleep Apnea Dynamics
Authors: Le Trung , Phat Huynh , Setty Arveity , Sharma Abhay 
Published: April 2024

Abstract

Introduction
The intermittent nature of obstructive sleep apnea's (OSA) pathophysiological processes is characterized by unpredictable fluctuations among OSA endotypes and phenotypes, reflecting the complex dynamics underlying this sleep disorder. It is challenging to character such intermittent behaviors.

Methods
We developed an enhanced version of Koopman spectral analysis, tailored to be robust against noise, a common issue in physiological data, to investigate the nonlinear intermittent behaviors in OSA. Our analysis dissects the complex, nonlinear physiological patterns of Heart Rate Variability (HRV) features, aiming to understand better the cardiorespiratory dynamic nature of OSA and its severity, as indicated by the apnea-hypopnea index (AHI). Utilizing the Apnea-ECG Database from Physionet.org, which includes records from 70 OSA patients with OSA annotation, we validated the model in the ability to characterize the intermittency behavior of the HRV features and the accuracy of using these features to detect OSA.

Results
The results demonstrate that the noise-robust Koopman spectral analysis effectively identifies key dynamical features in the pathophysiology of OSA. It revealed several previously unrecognized patterns and correlations in the data, offering more profound insights into the intermittent dynamics of OSA. The method showed high accuracy in distinguishing critical transitions between different severity levels of OSA.

Conclusion
The study successfully validates the efficacy of the noise-robust Koopman spectral analysis in analyzing complex systems like OSA. This approach provides an effective tool for understanding the intricate dynamics of pathophysiological processes, offering the potential for both diagnostic advancements and a better understanding of the underlying mechanisms of OSA.

Support (if any)
Huynh, P. K., Setty, A. R., Le, T. B., & Le, T. Q. (2023). A noise-robust Koopman spectral analysis of an intermittent dynamics method for complex systems: a case study in pathophysiological processes of obstructive sleep apnea. IISE Transactions on Healthcare Systems Engineering, 13(2), 101-116.
0288 In-vitro Model of the Microcirculation in OSA Based on Patient Profile
Authors: Hetta Ali , Badary Amr , Grosberg Anna , Khayat Rami 
Published: April 2024

Abstract

Introduction
Microcirculatory endothelial dysfunction is a critical marker of subsequent cardiovascular disease in the population. We recently reported a profile of obstructive sleep apnea (OSA) specific gene changes in the human microcirculation. We sought to develop a precise in-vitro model of the microcirculatory effects of OSA using an intermittent hypoxia (IH) exposure that produces similar gene profile changes to that observed in patients with OSA.

Methods
We selected a cell line of immortalized human microvascular endothelial cells (HMEC). Then, we estimated the oxygen tension exposure of the peripheral circulation during OSA assuming a decrease from 120 mmHg in the pulmonary arteries to about 30 mmHg in the microcirculation. During a single episode of apnea, alveolar partial pressure of oxygen (PAO2) and the PaO2 at the level of the pulmonary arteries decrease slightly by about 10%. Therefore, we selected a fluctuation between 30-40 mmHg as baseline (normoxia), and 25-30mmHg to correspond with this decrease during apnea. We used a gas treatment 6-well cell culture chamber (model NOX-E.5-GTC, Noxygen) and monitored the O2 levels in the medium (cell level) and in the gas space with fluorescent oxygen analyzer (Oxylite, Optronix). We selected a 300 second cycle length and an exposure duration of 6-8 hours (72 cycles).

Results
Compared to HMECs in control normoxia, HMECs exposed to the IH protocol showed a profile change similar to that reported in humans with OSA with significant increase in oxidative and inflammatory genes. There was significant upregulation in NADPH subunit NOX4’s from 0.0439 to 0.1570 relative copy number (RCN), p= 0.049. Endothelin (EDN-1) expression increased from 1.0330 to 2.4171 p= 0.002. Superoxide dismutase (SOD-1) increased from 2.1336 to 2.4600 (p= 0.028). Interleukin 8 increased from 0.0891 to 0.2744 (p= 0.020). Interleukin 1-B increased from 0.0193 to 0.0439 (p= 0.048). Additional changes included a decrease in nitric oxide synthase .

Conclusion
The selected IH protocol produced similar gene profile changes in HMECs as those found in patients with OSA before treatment. This model can serve to evaluate the pathways specific to OSA's impact on the microcirculation and to test pharmacological therapies to target OSA-specific vascular disease.

Support (if any)
R 56 127079
0289 Sleep Fragmentation and Estradiol Suppression Effects on Cardiometabolic Health in Women: An Experimental Model
Authors: Rahman Shadab , Grant Leilah , Scheer Frank , Klerman Elizabeth , Crawford Sybil , Sinrod Emily , Wiley Aleta , Kaiser Ursula , Joffe Hadine 
Published: April 2024

Abstract

Introduction
The risk of cardiovascular disease increases during the menopausal transition. In addition to estradiol decline during the transition, there is increased sleep fragmentation due to hot flashes, without reduced total sleep time. We examined the impact of sleep fragmentation and estradiol decline on cardiometabolic outcomes in an experimental menopause model implemented in premenopausal women.

Methods
We studied 38 healthy premenopausal women (mean age [±SD] 29.7±6.7 years) in a 5-night inpatient study under eucaloric conditions, once in the mid-to-late follicular phase (high estradiol) and again after gonadotropin-releasing hormone agonist-induced estradiol suppression in a subset (n=27). Each inpatient study involved 2 nights of unfragmented sleep followed by 3 nights of experimentally sound-induced sleep fragmentation to achieve ~1 h of wake after sleep-onset, without reducing total sleep time. Cardiometabolic endpoints (serum and vitals) were collected while fasted within 1 h of waking each morning. Values were normalized relative to each individual’s baseline (estrogenized, unfragmented sleep) and then compared between conditions using generalized linear mixed models.

Results
Sleep fragmentation across both estradiol states combined significantly lowered self-rated satiety by 13% (p=0.048), and increased hunger by 30%, although this difference did not reach significance (p=0.25), and leptin and acylated ghrelin levels were unchanged (both, p≥0.13). Sleep fragmentation significantly reduced triglycerides by 7% (p&lt; 0.01) and increased heart rate (HR) by 6% (p&lt; 0.01). All other cardiometabolic endpoints were unchanged by sleep fragmentation (p≥0.22). E2 suppression lowered self-rated satiety by 5% and increased hunger by 28%, but neither of these were significant (both, p≥0.28), and significantly lowered leptin levels by 6% (p=0.001), and increased ghrelin by 7% although this did not reach significance (p=0.13). Estradiol suppression across both sleep conditions combined significantly increased triglycerides, total cholesterol and HDL-C by 5% to 8% (all, p&lt; 0.01) but not LDL-C (p=0.18). Estradiol suppression significantly reduced HR by 4% (p&lt; 0.01) and trended to increase diastolic blood pressure by 3% (p=0.06), with no change in systolic blood pressure (p=0.16).

Conclusion
Findings support independent contributions of estradiol decline and sleep fragmentation during the menopause transition toward positive energy balance and adverse changes in cardiovascular disease risk factors.

Support (if any)
NIH R01AG053838
0292 Detecting Sleep Deficiency with Voice Biomarkers and Machine Learning
Authors: Zhang Boyu , Ronda Joseph , Yuan Robin , Duffy Jeanne , Czeisler Charles 
Published: April 2024

Abstract

Introduction
Accurate biomarkers of insufficient sleep have been a central interest of sleep scientists. Given advancements in artificial intelligence, researchers have explored non-invasive digital biomarkers from human voices. In this study, we conducted a within-participant counterbalanced controlled trial of chronic sleep restriction (CSR) and leveraged machine learning to investigate voice biomarkers for detecting sleep deficiency.

Methods
Healthy young adults completed a 32-day in-patient protocol. The protocol included 5 days of baseline 8-hour time-in-bed (TIB) followed by 5 days of CSR (5-hour TIB), during which their light exposure, activity, and diets were controlled. Every 4 hours during waking episodes, a voice measurement (VM) was administered via computer. During each VM, the participant read 10 sentences at their habitual volume, pitch, and pace. The sentences were phonetically balanced and tailored for professional speech quality assessment. Each VM used different sentences to prevent memorization. An omnidirectional microphone with an adjustable stand was used. 85 common acoustic features, such as fundamental frequency, formants, and mel-frequency cepstral coefficients, were extracted from each VM. Additionally, 11 features from the Cepstral Spectral Index of Dysphonia were extracted, forming a 96-dimensional vector. Within each participant, the vectors were z-scored to remove personal vocal traits. After dimension reduction via principal component analysis, under a leave-one-out procedure, we trained a support vector machine (SVM) to classify recordings into those taken during CSR or baseline. We tested the SVM on one unseen participant every iteration.

Results
After excluding low-quality data, we analyzed 196 VMs (100 during CSR and 96 during baseline) from 5 participants, including 1 non-native English speaker. The SVM reached a sensitivity of 0.74 (95% CI: 0.71-0.78), a specificity of 0.71% (95% CI: 0.68-0.75), and an area under the ROC curve of 0.76 (95% CI: 0.73-0.80) in classifying CSR vs. baseline conditions.

Conclusion
These preliminary findings reveal that vocal characteristics may represent a target for non-invasive, objective sleep deficiency biomarkers. Additional studies in both controlled conditions and field settings should be carried out to explore whether features of the human voice can be developed for non-intrusive monitoring of sleep status for sleep disorders patients, fitness-for-duty evaluations, and other applications in ambulatory settings.

Support (if any)
0294 Performance of Commercial Wearable Devices for Tracking Irregular Sleep Schedules
Authors: Chinoy Evan , Cuellar Joseph , Gordy Prayag , Jameson Jason , Markwald Rachel 
Published: April 2024

Abstract

Introduction
Many new popular commercial wearable devices perform well at tracking sleep/wake patterns, often equaling or exceeding the sleep-tracking performance of research-grade actigraphy. However, commercial wearables have not been subjected to extensive testing under irregular sleep schedules, such as split sleep and daytime sleep/naps, common in shift-working and operational populations. Therefore, to continue uncovering their real-world strengths/limitations, we evaluated the sleep-tracking performance of commercial wearables in people with irregular sleep schedules.

Methods
Participants were 12 healthy young adults (6 men, 6 women; 28.3±4.8 years, mean±SD) with habitual irregular sleep schedules, often due to shift work, sleeping or napping during daytime hours at least twice weekly. They completed sleep diaries and used actigraphy (Philips Respironics Actiwatch 2) and a set of wearables for 1 week under unrestricted home sleep conditions. Wearables included the Garmin Fenix 7 (GF) and Oura Rings (OR; generations 2 and 3). Device performance was compared with an electroencephalography headband (Dreem 3; Beacon Biosignals) and sleep diaries. Analyses included epoch-by-epoch agreement (sensitivity [for sleep] and specificity [for wake]) and summary bias comparisons for time in bed (TIB) and total sleep time (TST).

Results
Device sensitivity and specificity, respectively, were Actiwatch (0.92, 0.44), GF (0.96, 0.35), and OR (Gen-2: 0.92, 0.64; Gen-3: 0.93, 0.60). Device mean biases for TIB and TST were low for Actiwatch and both ORs but were higher and with significant proportional bias for GF. This appeared to be driven by GF combining multiple sleep episodes into single long episodes, which produced very long sleep recordings on those days when split sleep occurred.

Conclusion
Overall device sensitivity was high and specificity was low to medium, similar to previously known performance limitations of actigraphy and commercial wearables. However, OR had ~20% higher specificity than in our previous testing, suggesting improved wake-tracking performance in newer OR models/algorithms. Like previous GF models we tested, GF again underperformed in specificity and summary TST tracking. This underperformance raises concerns for real-world uses of GF with its current algorithm, especially regarding the finding that extreme summary bias values sometimes occurred when GF combined multiple sleep episodes/naps during split sleep schedules.

Support (if any)
Office of Naval Research, Code 34
0295 Influence of Sleep Regularity, Chronotype, and Sleep Duration on Daytime Sleepiness Caused by Sleep Disorders
Authors: Chellamuthu Vidhya , Gorski Brandon , Mills Rajasi , McGhee Leah , DeFranco Susan , Aloia Mark , Molina Gary Garcia 
Published: April 2024

Abstract

Introduction
Excessive daytime sleepiness is characterized by the difficulty of staying awake during daytime. The goal of this research is to examine the role of regularity, sleep duration, and chronotype on the association between sleep disorders and daytime sleepiness. To do this, we leveraged the Sleep Number platform to acquire longitudinal objective sleep with survey data from a single point in time.

Methods
An IRB-approved survey, which included questions about diagnosed sleep disorders and daytime sleepiness, was presented to a cohort of Sleep Number customers in the June 12 to 26, 2023 period. Objective data including sleep duration, bedtime, restful sleep duration, mean breathing rate, and heart rate collected between May 1 and June 30, 2023 were used along with survey responses. Individual sleep regularity and chronotype were quantified using objective data. Regularity was categorized into regular or irregular if bedtime variability was lower or greater than 60 minutes. Chronotype was categorized into early, intermediate, or late if mean sleep onset time was before 10 pm, between 10 and 11:59 pm, or after 12 am. Odd-ratios (OR) were used to quantify the influence of regularity, chronotype, and sleep duration on daytime sleepiness moderated by sleep disorders.

Results
The responder count was 22,082 (9530M/12479F). Men were 56.6 (SD 13.9) and Women 54.9 (SD 13.8) years-old on average. Daytime sleepiness was significantly associated with the presence of untreated sleep disorders (insomnia, apnea, and RLS). Individuals being treated for apnea and insomnia demonstrated significantly less daytime sleepiness. Healthy individuals showed a significant association between early chronotype and longer sleep duration with reduced daytime sleepiness. Individuals with apnea who receive treatment showed a significant association between longer sleep and reduced daytime sleepiness. In the case of insomnia, regularity, chronotype or sleep duration do not moderate any significant relationship with daytime sleepiness. For RLS longer sleep duration leads to lower daytime sleepiness.

Conclusion
Daytime sleepiness is significantly associated with any untreated sleep disorders. Regularity, chronotype and sleep duration had limited influence on daytime sleepiness caused by sleep disorders. Healthy individuals had lower daytime sleepiness if they had an earlier chronotype and longer sleep duration.

Support (if any)
0296 The Relationship Between Sleep Metrics, Age and Sex in a Large Cohort Study
Authors: Patterson Matthew 
Published: April 2024

Abstract

Introduction
The aims of the study are to investigate the relationship between sleep metrics, age and sex in a large cohort study

Methods
Wrist acceleration data (GT9x, ActiGraph) was collected from 10,175 subjects in the National Health and Nutrition Examination Survey (NHANES) 2013-2014 for up to 8 days. Data from the tri-axial accelerometer was sampled at 80hz and was processed using the sleep analysis pipeline from ActiGraph. The sleep analysis pipeline first consisted of wear detection, followed by total sleep opportunity detection and then finally sleep / wake classification within the total sleep opportunity. The total sleep opportunity algorithm detects one sleep opportunity per 24-hour cycle (in noon-to-noon windows) and allows for up to 60 minutes of continuous sleep disruption within the sleep opportunity. The following sleep metrics were calculated in each total sleep opportunity using the DACNN sleep / wake classification algorithm: total sleep time (TST), wake after sleep onset (WASO) and sleep efficiency (SE). These sleep metrics were combined with age and gender data from the study to perform a Pearson product correlation (r).

Results
For females in the study (n=3790), TST had a Pearson r of -0.285, WASO had a Pearson r of 0.149 and SE had a Pearson r of 0.058. For males in the study (n=3529), TST had a Pearson r of -0.300 versus age, WASO had a Pearson r of 0.192 versus age and SE had a Pearson r of -0.005 versus age.

Conclusion
Sleep measures showed low correlation to age in both the male and female cohorts. This is contrary to the information that sleep quality declines as individual ages. Perhaps the participants in this cohort were healthy enough that decline in sleep metrics was not observed.

Support (if any)
This research was funded by ActiGraph L.L.C.
0299 An Examination of Discrepancies in Sleep Device Data Captured by Sleep Ring, Fitbit, AndActigraphy
Authors: Moise Rhoda , Oliveira Bruno , Blanc Judite , Rogers April , Seixas Azizi , Jean-Louis Girardin 
Published: April 2024

Abstract

Introduction
Wearable devices have improved access to sleep data by empowering patients with greater control over their information. However, the choice of the best wearable for sleep measurement remains uncertain. This study investigated variations in sleep data collected by Sleep Ring, Fitbit, and Actigraphy in a diverse US group of Black adults.

Methods
Wearables descriptive statistics were calculated for sleep efficiency (% by time in bed/ time sleep), REM duration (in minutes), awakening (count), and total sleep duration (in minutes) where data was available. One-sample t-tests were conducted for sleep efficiency (Sleep Ring and Actigraphy), REM duration (Sleep Ring and Fitbit), and awakening (Fitbit and Actigraphy). ANOVA was conducted for total sleep duration (Sleep Ring, Fitbit, Actigraphy). Significance was specified as α=0.05.

Results
Participants (Sleep Ring N=199, Fitbit N=147, Actigraphy N=202) were predominantly female (71%, Mage=48.9) compared to male (28%, Mage=50.2). Using the Insomnia Severity Index, 35% were insignificant, 37% met subthreshold, 23% exhibited moderate severity, and 5% reported severe insomnia. Sleep efficiency for Sleep Ring (M=77%, SD=10.4; t(198)=104.6, p&lt;.001) was significantly different than Actigraphy (M=93%, SD=3.3; t(201)= 397.7, p=&lt;.001). REM duration for Sleep Ring (M=62 minutes, SD=29; t(198)=30.0, p&lt;.001) was also significantly different Fitbit (M=70 minutes, SD=26; t(142)= 31.6, p&lt;.001). Additionally, there was a significant difference in awakening count in Fitbit (M=15 times, SD 7; t(146)=26.6, p&lt;.001) compared to Actigraphy (M=9 times, SD=4; t(201)= 28.7, p&lt;.001). ANOVA indicated an effect of sex on sleep duration for all devices: Sleep Ring [M=368 minutes, SD=106; F(1,197)= 5.4,p=.021], Fitbit [M=305, SD=89; F(1,145)=13.4,p&lt;.001] and Actigraphy [M=374 minutes, SD=163; F(1,198)=4.9,p=.027).

Conclusion
Differences among wearable devices exist. Actigraphy may overestimate sleep efficiency, while Sleep Ring may provide poor estimates. REM duration is similar in Sleep Ring and Fitbit but more variable in Sleep Ring. Sleep duration ranked highest in Actigraphy, but also exhibited the highest variance/deviation, followed by Sleep Ring and Fitbit. Fitbit is more sensitive in detecting awakenings compared to Actigraphy. Future research should exploresleep health parameters across wearable devices to ensure accuracy in Blacks, considering discrepancies in metrics potentially due to differences in skin tone and the impact of refractory lights

Support (if any)
NIH R25HL10-5444; R01HL142066 and R01AG067523.
0300 Interim Analysis from the Oura Validation Study: Examining Sleep Staging from Oura Rings Worn Simultaneously
Authors: Robbins Rebecca , Sullivan Jason , Gilmore Katherine , Shaw Samantha , Benz Abigail , Weaver Matthew , Barger Laura , Quan Stuart , Czeisler Charles , Duffy Jeanne 
Published: April 2024

Abstract

Introduction
Sleep tracking using consumer devices is increasingly prevalent in the population. Given this interest, it is essential that data produced by these monitors is reliable and accurate. In a single night study with the primary aim of validating the Oura Ring in comparison to polysomnography (PSG), participants wore Oura Rings on their right and left hand. This interim analysis explores the correlation between the rings with respect to total sleep time (TST) and sleep staging.

Methods
Participants (n=20) underwent single-night PSG in the Brigham and Women’s Hospital Center for Clinical Investigation. Each participant wore two Gen3 Oura Rings, one on each index finger, both running the Oura Sleep Staging Algorithm 2.0. We computed within-subject nightly totals for TST and sleep stages (light sleep; deep sleep; and Rapid Eye Movement, REM sleep) as detected by each ring. Then, we computed pairwise correlation coefficients for sleep and sleep staging data between the rings.

Results
Participants were 75% female and 25% male; 20% Asian, 10% Black/African American, and 60% White; 15% were Hispanic. Average age was 30.7±7.8 years. In-lab bedtimes ranged from 9:15 pm–1:40 am and waketimes from 5:15 am–9:40 am. On the Beck Anxiety index, most participants (95%) demonstrated minimal anxiety. No participants met criteria for insomnia (Athens Insomnia Scale), sleep apnea (Berlin Sleep Questionnaire), or Restless Legs Syndrome. On the Pittsburgh Sleep Quality Index, most participants reported good sleep quality (90%). Correlation between the rings was 0.90 (p&lt; 0.001) for TST, 0.85 (p&lt; 0.001) for wake. 0.83 (p&lt; 0.001) for light sleep, 0.86 (p&lt; 0.001) for deep sleep, and 0.86 (p&lt; 0.001) for REM sleep.

Conclusion
We observed a high correlation between TST and sleep staging data from Oura Rings worn on the left and right hands simultaneously, similar to those observed between Registered Polysomnographic Technologists. We plan to examine how Oura sleep staging relates to PSG, and whether agreement is associated with hand dominance.

Support (if any)
This project was funded by Ouraring Inc. and the studies were carried out at the BWH CCI, with support from Harvard Catalyst, The Harvard Clinical and Translational Science Center (NIH UL1TR002541). Additional support from the NHLBI (K01HL150339).