bluuebunny/crossref_metadata_2025_split huggingface dataset:
0254 Diagnosis of Obstructive Sleep Apnea using Koopman Spectral Analysis
Authors: Le Trung , Huynh Phat 
Published: May 2023

Abstract

Introduction
Koopman operator theory and the Hankel alternative view of the Koopman (HAVOK) model have been widely used to investigate the chaotic dynamics in complex systems. Although the statistics of intermittent dynamics have been evaluated in the HAVOK model, they are not adequate to characterize intermittent forcing. We proposed the Koopman spectral analysis method to characterize the intermittent transition between apeanic and non-apneaic episodes to predict impending OSA episodes.

Methods
Apnea-ECG Database from Physionet.org was selected to investigate the nonlinear intermittent behaviors of OSA disorder. The data set has 70 records of OSA patients, and the recordings vary from 7 hours to nearly 10 hours each. Each recording consists of a continuous digitized ECG signal and apnea annotations. First, the eigen time-delay coordinates were reconstructed from the heart rate variability (HRV) features. Next, the last eigen time-delay coordinate was modeled as an intermittent forcing component during apneic episodes. Finally, the forcing statistics in OSA were estimated to characterize the intermittency of the pathophysiological process.

Results
The statistics of the forcing in OSA demonstrated the fat-tailed distribution of the intermittent forcing of the underlying OSA pathophysiological process. The pooled means and standard deviations of the burst duration and the inter-burst duration across OSA patients were also calculated to be 1.73±0.26 minutes and 11.71±2.69 minutes. Scalogram amplitude and spectral decomposition of the wavelet transform exhibited various predominant frequencies and dynamics modes associated with apneic events.

Conclusion
We have reported the data-driven HAVOK model to obtain an intermittently forced linear system of chaos for the OSA pathophysiological processes The proposed method has been validated on 70 records of OSA patients from Apnea-ECG database. The forcing signal can predict the intermittent transient events, such as the transition from normal to apneic episodes in OSA case.

Support (if any)
Huynh PK, Setty AR, Le TB, Le TQ. 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. 2022 Nov 1:1-6.
0255 Female Mice Are Less Susceptible to the Effects of Diet-Induced Obesity on Sleep and Sleep-Disordered Breathing
Authors: Kim Lenise , Pho Huy , Pham Luu , Polotsky Vsevolod 
Published: May 2023

Abstract

Introduction
Diet-induced obesity (DIO) in male mice is a well-established model of sleep-disordered breathing (SDB). Male DIO mice manifest hypoventilation, increased apnea severity, and sleep fragmentation. The effects of DIO on breathing during sleep and sleep architecture in females have been insufficiently investigated. We hypothesized that female mice are less susceptible to the detrimental effects of DIO on sleep and SDB compared to males.

Methods
Full-polysomnographies were performed in 15 lean female C57BL/6J and 14 female DIO-C57BL/6J mice using a whole-body plethysmography chamber. Ventilatory response to an arousal was calculated as ratio of the average minute ventilation (VE) during each arousal (2.5s) relative to the baseline VE. Breathing stability was measured using Poincaré plots of VE. Mice were exposed to 8% CO2 to measure the hypercapnic ventilatory response (HCVR). Mice underwent 24-h metabolic studies.

Results
Obesity was associated with hypometabolism, indicated by reduced oxygen consumption (VO2) and CO2 production in DIO mice. VE in 8% CO2 and HCVR were significantly suppressed suggesting a decreased CO2 sensitivity in obese animals. NREM sleep duration was reduced, but REM sleep was preserved. Arousal frequency and VE during NREM and REM sleep were similar between groups. However, apnea severity was 4-fold augmented in lean females compared to DIO mice (14.7±3.9 vs 74.2±11.9 events/h; P&lt; 0.001). In lean mice, the increased frequency of apneas was mainly attributed to sleep fragmentation since the events were predominantly post-arousals. Breathing stability during sleep was significantly increased in lean mice. Ventilatory response to an arousal was 30% increased in lean females compared to DIO group.

Conclusion
Our results suggest potential sex differences in the effects of DIO on breathing during sleep and sleep architecture. DIO in females appears to be less detrimental regarding SDB severity. The mechanisms underlying the augmented SDB severity in lean females are unknown, but an exacerbated CO2 responsiveness during sleep related to sleep fragmentation could play an important role.

Support (if any)
American Heart Association Postdoctoral Fellowship Award 828142, NHLBI grant NIH R01s HL128970, HL133100, and HL13892, American Academy of Sleep Medicine Foundation 238-BS-20, and American Thoracic Society Unrestricted Award.
0256 Modeling Individual Sleep Needs from Wearable-Device Data
Authors: Shah Palak , Braun Rosemary 
Published: May 2023

Abstract

Introduction
There are currently thought to be two processes that interact to regulate one’s sleep/wake cycle: a sleep homeostat, which depends on the amount of time you have been awake; and the circadian rhythm, an internal 24-hour molecular clock present in nearly every cell of the body. Both processes differ between individuals, whether that’s from a decision to pull an all-nighter or from a genetic inclination towards being an “early bird” or a “night owl”. This project focuses on building a model that can predict how sleep disruptions impact an individual’s sleep cycle, and the ability to recover from these disruptions.

Methods
A “two-process” model with the capability to simulate a theoretical sleep-wake cycle with disruptions was implemented in Python, and is being tuned using machine learning (ML) on actigraphy data. Actigraphy measures motion using a noninvasive accelerometer embedded into a wearable-device (e.g. FitBit), allowing for the study of sleep activity patterns in a real-world setting. Data was collected from approximately 20,000 individuals, who reside in 121 different countries. ML algorithm was trained and tested on synthetic data generated from the “two-process” model, and then applied to the actigraphy dataset to evaluate prediction accuracy.

Results
Our model has shown that recovery from sleep disruptions can be modeled mathematically, and can be individualized using actigraphy data. The mathematical model was able to successfully simulate the “two-process” interaction, and, by sweeping parameter values, those that yielded a single sleep bout of 8-9 hours/night were identified. ML algorithm tuning with actigraphy data is ongoing.

Conclusion
Our results suggest that it is possible to predict the sleep patterns of individuals despite disruptions in their schedules from sleep disorders, travel, work, or social and family commitments. Future research should focus on optimization of sleep in individuals using the informational output of our model, and extending it to incorporate the behavioral and/or clinical changes that have proven to improve an individual’s sleep. By incorporating this additional set of data, the resulting model can not only predict the impact of disruptions, but also provide recommendations for prevention and recovery based on an individual’s input about their lifestyle and their sleep.

Support (if any)
0258 Altered sleep regulation in a mouse model of Parkinson’s disease
Authors: Summa Keith , Jiang Peng , Gonzalez-Rodriguez Patricia , Surmeier James , Vitaterna Martha , Turek Fred 
Published: May 2023

Abstract

Introduction
Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by pathognomonic motor abnormalities that lead to significant disability and morbidity in advanced stages. PD is also associated with sleep abnormalities such as increased fragmentation, insomnia, and excessive daytime sleepiness. Little is known of the underlying mechanisms, in part due to the limited ability of existing mouse models to adequately replicate human PD patient sleep phenotypes. Here we characterize sleep-wake behavior in an innovative genetic mouse model of PD (MCI-Park) targeting a mitochondrial complex I gene (Ndufs2), which experiences progressive dopaminergic neuron loss and displays motor phenotypes resembling those seen in human PD.

Methods
Undisturbed sleep-wake behavior during 12 hr light: 12 hr dark cycles was evaluated in MCI-Park mice during prodromal stages (6-8 weeks of age) and later parkinsonian stages (14-18 weeks of age) as well as in age-matched wildtype littermate controls. Electroencephalographic (EEG) and electromyographic (EMG) recording electrodes were surgically implanted using sterile technique in male and female MCI-Park and wildtype mice. After one week of recovery and acclimation to recording chambers, EEG and EMG were recorded continuously and sleep phenotypes were analyzed.

Results
In prodromal stages, MCI-Park mice exhibited similar overall sleep state amounts compared to wildtype mice, however there were significantly more state shifts as well as longer bouts of wakefulness and shorter bouts of non-REM (NREM) sleep. As mice progressed to parkinsonian stages, these fragmentation measures worsened. In addition, parkinsonian MCI-Park mice experienced a significant increase in wakefulness, a significant reduction in NREM sleep amount, and alterations in REM sleep, with pronounced reductions in REM sleep amount and number of REM bouts. Power spectral analysis revealed consistent decreases in theta2 (also known as alpha) power across all sleep-wake states in MCI-Park mice.

Conclusion
These results indicate that mice harboring a genetic defect causing selective disruption of dopaminergic neurons exhibit progressive sleep abnormalities that resemble sleep disturbances in PD patients. This model thus offers an opportunity to evaluate mechanisms underlying and therapeutic strategies targeting altered sleep in PD patients.

Support (if any)
This work was supported by the Department of Defense award number W81XWH-21-0582 (F.W.T. and D.J.S.).
0259 Associations between exercise, sleep, and mood during pregnancy
Authors: McCarthy Traci , Velez Sarah , Spaeth Andrea 
Published: May 2023

Abstract

Introduction
Sleep and mood disturbances are common complaints among women during pregnancy. Exercise is a non-pharmacological intervention that may help improve sleep and mood; however the relationship between these variables in pregnancy is unclear. The purpose of the current study is to determine if meeting exercise guidelines during pregnancy associates with improved sleep and mood.

Methods
Women (n=31, 32.5±4.3y) with healthy pregnancies were enrolled in the study during their third trimester (28-32 weeks gestation). Participants completed the Pregnancy Physical Activity Questionnaire (PPAQ), Pittsburgh Sleep Quality Index (PSQI), and PROMIS mood questionnaires and then wore a wrist actigraph and completed a daily sleep diary for two weeks. Participants who reported meeting exercise guidelines (&gt;150 minutes moderate-to-vigorous physical activity [MVPA]/week, EX, n=14, pre-pregnancy BMI 23.7±0.9) were compared to those who reported not meeting these guidelines (&lt; 90 minutes/week, SED, n=17, pre-pregnancy BMI 23.8±0.9). Multivariate ANOVAs were conducted to compare groups on outcome measures and correlations were conducted between exercise, sleep and mood variables.

Results
Exercise guideline groups did not differ in subjective sleep quality (PSQI Global, p=0.23), objective measures of sleep behavior (sleep duration, efficiency wake after sleep onset, and fragmentation, ps&gt;0.10) or in self-reported anger, anxiety, depression or positive affect (ps&gt;0.25). Greater total daily activity associated with improved sleep efficiency (p=0.07) and less wake after sleep onset (p=0.07) but did not associate with mood outcomes. Less wake after sleep onset and sleep fragmentation were associated with higher positive affect (ps&lt; 0.05) and less depressive mood (ps&lt; 0.07).

Conclusion
Meeting exercise guidelines (150 minutes MVPA/week) did not associate with improved sleep or mood in the third trimester of pregnancy. However, greater daily physical activity associated with improved sleep, and improved sleep associated with better mood. Women are particularly interested in non-pharmacological treatment options during pregnancy and these preliminary results support the development of behavioral interventions aimed at increasing daily physical activity in this population.

Support (if any)
American College of Sports Medicine Foundation Research Grant awarded to TM.
0263 Feasibility of Measuring Sleep Spindles Using a Wearable EEG Headband in Adolescents with Neurodevelopmental Disorders
Authors: O'Hora Kathleen , Saletin Jared , Kushan-Wells Leila , Bearden Carrie 
Published: May 2023

Abstract

Introduction
There is compelling evidence that sleep spindles, electroencephalogram (EEG) signatures of non-rapid eye movement sleep, are reliable markers of thalamocortical dysfunction in Schizophrenia. While promising, this research is limited by traditional sleep EEG methods, which cultivate small, non-representative samples. Wearable EEG headbands could be used to study naturalistic sleep across several nights in individuals who cannot tolerate standard sleep assessments, but this has yet to be tested. We aim to test feasibility of using a dry-EEG headband to measure sleep spindles in adolescents with a neurodevelopmental disorder, 22q11.2 Deletion Syndrome (22q11DS).

Methods
Adolescents with 22q11DS (n=7; 3 Females; Age: 15-28 years) were asked to sleep in their own bed wearing a dry-EEG headband (Dreem 3). Feasibility of data acquisition was determined by the ability of the participants to record at least 3 consecutive nights. We performed visual artifact detection and examined the ability to detect sleep spindles using the well-validated Ferrarelli Spindle Detection Algorithm on the F7-O2 electrode.

Results
All subjects completed at least 3 nights of recording (range: 3-5 nights), suggesting the device was well-tolerated. The data contained minimal artifact; 17 out of the 22 nights available for analysis contained artifact in &lt; 20% of sleep epochs. The best night contained artifact in only 6% of sleep epochs. Sleep spindles were successfully detected using an automatic detector (M= 1.92 spindles/minute).

Conclusion
This pilot study suggests dry-EEG headbands offer a feasible method to measure sleep spindles in individuals with neurodevelopmental disorders. This new technology could allow for scalable biomarkers of atypical neurodevelopment associated with psychiatric disorders.

Support (if any)
RO1 MH085953; U01MH10171; UCLA Center for Autism Research Pilot Grant; Dreem Coup de Coeur Award
0264 Untangling Correlations Between Positive Affect and Light and Activity Data Using Deep Learning
Authors: Wheeler Bradley , Hasler Brant , Wallace Meredith 
Published: May 2023

Abstract

Introduction
Research has found activity and light data continuously captured via wearable devices predict psychological outcomes. Understanding which sources are most predictive could suggest which biological or environmental factors most impact outcomes. Existing work has largely relied on summary measures of these sources. This may overgeneralize important nuances in the sources that are correlated to the outcomes. We utilized deep learning to predict daily affected based on raw actigraphy and light data to determine which sources are most predictive of daytime positive affect.

Methods
We modeled the daily mean (DMPA) and standard deviation (DSDPA) of positive affect using actigraphy and light data from a cohort of 172 adolescents who completed an ecological momentary protocol for 7-8 days. Positive affect was measured via PANAS-SF and administered via smartphone 5-6 times daily. The outcomes are predicted using the actigraphy and light data during the environmental night (time between astronomical twilight and dawn) prior to each outcome. Convolutional neural networks are used to predict the outcomes using all permutations of actigraphy, white light, and red-green-blue (RGB) light data.

Results
The mean (standard deviation) of the DMPA and DSDPA outcomes were 6.53(1.60) and 1.28(1.15) respectively. Using white and RGB light we could predict DMPA and DSDPA with a root mean squared error (RMSE) of 1.59 and 1.08 respectively (i.e., within 0.99 and 0.93 deviations respectively).

Conclusion
Our work indicates that white and RGB light best predict positive affect. This may indicate that light-based environmental night factors have a larger impact on daily affect than activity-based factors. Most results were within a few hundredths of a deviation from each other, and the channels that best predicted affect differed depending on whether DMPA or DSDPA was modeled. Our methods provide a way to predict affect from raw data and suggest which environmental and biological data have clinical implications towards affect. Important next steps include using time periods related to individual’s dim light melatonin onset and comparing findings from deep learning to those based on summary measures.

Support (if any)
CARRS Pilot Grant (Wheeler), R01-AA025626 (Hasler), R01-DA044143 (Hasler), RF1-AG056311-04 (Wallace)
0266 An mHealth intervention to deliver personalized messages to increase adherence to OSA care among transportation workers
Authors: Robbins Rebecca , Quan Stuart , Troxel Andrea , DiClemente Ralph , Barger Laura , Rapoport David , Viswanath Kasisomayajula , Jean-Louis Girardin , Czeisler Charles 
Published: May 2023

Abstract

Introduction
Transportation workers are at risk for obstructive sleep apnea (OSA), yet OSA is commonly undiagnosed in this population. In a two-phase study, we design and evaluate a tailored, mobile health (mHealth) intervention aimed at achieving OSA awareness, including changes in OSA beliefs, among operators of large highway maintenance vehicles.

Methods
In Phase 1, we recruited highway maintenance vehicle operators employed at worksites in the US northeast for focus groups to examine OSA beliefs and barriers. Results of the Phase 1 focus groups were used to inform the tailored intervention. In Phase 2, we evaluated the tailored approach, “Sleep Strong, Live Long,” in a mini-RCT. Worksites were randomized to intervention (Sleep Strong, Live Long) or control. Employees at both worksites completed baseline questionnaires and follow-up questionnaires 2-months post-baseline. Employees at the intervention worksite received a 30 minute in-person education session, access to an interactive website including video success stories, then follow-up text messages over 2 months as part of the Sleep Strong, Live Long program. The control condition received the intervention after completing follow-up surveys. We compared responses on the follow-up surveys between intervention and control conditions using linear regression with baseline responses as covariates.

Results
Phase 1 themes included common sleep complaints, inconsistent work schedules, and misconceptions about OSA (n=45). 75% of Phase 1 participants reported resistance to OSA behavior change. Phase 2 mini-RCT participants (n= 38) were on average 43.4 (sd=12.1) years old. Participants were 86.8% male and 67.6% reported an annual household income of $50,000 or more. Exposure to the intervention was associated with an improvement in OSA literacy (b=0.14, p=0.037), but not OSA self-efficacy (b=0.12, p=0.330) or OSA readiness to change (b=0.76, p=0.336).

Conclusion
We provide preliminary support for improving OSA literacy among a sample of transportation workers. Future research may evaluate the tailored program in a larger sample of workers and with a longer follow-up period to consider whether OSA literacy and self-efficacy mediate actual OSA behaviors, such as changes in OSA evaluation and treatment.

Support (if any)
This work was supported by the National Heart, Lung, and Blood Institute (NHLBI) at the National Institutes of Health (NIH, Grant # K01HL150339).
0268 Mathematical recursive model presenting a formula to optimize apnea-hypopnea index based on sleep stage and sleep position
Authors: Im SangHyuk , Lee Chang-Hoon , Im KyoungBin 
Published: May 2023

Abstract

Introduction
Despite apnea-hypopnea index (AHI) being the gold standard in diagnosing obstructive sleep apnea, AHI can be further optimized if AHI is differentiated according to sleep stages/positions, then integrated with weighted time. The primary aim of this study is to demonstrate the mathematical modeling process from which ‘projected AHI’ (pAHI) can be calculated.

Methods
With the hypothesis of the nth projected AHI, expressed as AHI_n, being the gold standard and AHI_0 being test results, using AHI of 5 as cutoffs, subjects were grouped into a confusion matrix of TN_n, FP_n, FN_n and TP_n. Through recursive optimization, information obtained from AHI_0 and AHI_n was used to calculate AHI_(n + 1). The coefficients of the equation to calculate AHI_(n + 1) consisted of A_(n + 1), B_(n + 1), C_(n + 1), and D_(n + 1), each representing the % of total time of each of the four types of sleep (REM supine, REM non-supine, NREM supine, NREM non-supine) out of the people in the groups TN_n and FP_n. We tested a closed polysomnographic dataset of 337 subjects to determine the point at which all four coefficients would stay constant, producing the ideal pAHI of the dataset.

Results
Each of the four coefficients of the equation converged at n = 3. At n = 3, A_3 = 0.090, B_3 = 0.087, C_3 = 0.433, and D_3 = 0.390. The patients in the groups TN_2 and FP_2 turned out to be the same patients that were in the groups TN_3 and FP_3, explaining why each of the four coefficients of the equation converged at n = 3, and continuously onwards. As a result, it logically follows that for all n &gt; 2, the formulas of AHI_n were determined to be equal in this closed dataset.

Conclusion
Ideal pAHI is not a fixed formula. Instead, a recursive optimization model can generate an ideal formula in a big data set when each coefficient of the equation converges. In addition, the recursive optimization model used in our pAHI can be particularly useful in machine learning as new data sets are prospectively added to the existing data.

Support (if any)
n/a