bluuebunny/crossref_metadata_2025_split huggingface dataset:
0375 Culturally Consistent Diet Among Individuals of Mexican Descent at the Us-Mexico Border is Associated with Sleep Duration and Quality
Authors: Ghani S , Delgadillo M E , Killgore W D , Wills C C , Grandner M A 
Published: May 2020

Abstract

Introduction
Previous studies have shown that people who consume culturally consistent foods have improved cardiometabolic profiles. Few studies have examined whether this finding extends to sleep health.

Methods
Data were collected from N=100 adults (age 18-60, 53% female) of Mexican descent in the city of Nogales, AZ (66% not born in the US, 33% 1st-generation). Surveys were presented in English or Spanish. Acculturation was assessed with the Acculturation Scale for Mexican-Americans (ARSMA-II), which has separate scales for Mexican and Anglo acculturation (subscale range 0-4). A supplemental ARSMA item asks how often “My family cooks Mexican foods.” Responses were coded as either frequent or infrequent. Insomnia was assessed with the Insomnia Severity Index (ISI), Sleepiness with the Epworth Sleepiness Scale (ESS), Sleep quality with the Pittsburgh Sleep Quality Index (PSQI), and Sleep duration and sleep medication use with PSQI items. Regression analyses examined these outcomes relative to whether individuals frequently consumed Mexican foods. Covariates included age, sex, and acculturation scores. Parental education level was also included, as an indicator of childhood socioeconomic status and since food culture likely involves parents.

Results
Regular consumption of Mexican foods was associated with 1.41 more hours of sleep, on average (95%CI 0.19,2.62, p&lt;0.05). It was also associated with a decreased likelihood of snoring (oOR=0.25; 95%CI 0.07,0.93; p&lt;0.05). No differences were seen for PSQI, ISI, or ESS score.

Conclusion
Individuals of Mexican descent at the US-Mexico border who regularly consume culturally consistent food report overall more sleep and less snoring. Previous studies show that Mexican acculturation may be associated with improved sleep sufficiency; it is possible that this reflects an overall healthier lifestyle that also includes a culturally consistent diet. Further studies would be beneficial to help determine the role acculturation plays in sleep and diet and how it effects cardiometabolic risk.

Support
Dr. Grandner is supported by R01MD011600. This work was supported by a UAHS grant.
0388 Predictors of Incident Reduced Sleep Efficiency in Community-Dwelling Older Women
Authors: Vo T , Blackwell T , Kats A , Langsetmo L , Taylor B , Schousboe J , Redline S , Stone K , Smagula S , Chu H , Rodriguez R , Schommer J , Carlson A , Ensrud K 
Published: May 2020

Abstract

Introduction
There is a paucity of longitudinal studies with sleep efficiency (SE) as an outcome measure. Our objective was to examine potential risk factors for incident reduced SE among community-dwelling women in late life.

Methods
We studied 700 women (mean age 82.5 [SD=3.0] years) with a SE ≥70% at the Year 16 (2002-04) visit of the Study of Osteoporotic Fractures with a follow-up measure of SE at the Year 20 (2006-08) visit. SE (percentage of time sleeping while in bed) at both visits was measured using a wrist actigraph with data collected for an average of four 24-hour periods. Women were classified as having incident reduced SE if they had SE &lt;70% at Year 20. Logistic regression was used to estimate the associations between potential risk factors (demographics, lifestyle, use of medications, self-reported medical conditions, functional impairment, frailty, mental and physical health) at Year 16 and reduced SE at Year 20. The association of each candidate risk factor with reduced SE at Year 20 was examined in models adjusted for age, clinical site and continuous SE at Year 16. Candidate risk factors with Benjamin Hochberg false-discovery rate q-values &lt;0.10 were included in a final multivariate model.

Results
Among the 700 eligible women, 62 (8.9%) developed incident reduced SE between the Year 16 and Year 20 visits. After adjusting for age, site and baseline SE, antidepressant use [OR=3.06; 95% CI: 1.50-6.25], benzodiazepine use [OR=2.97; 95% CI: 1.30-6.80] and the presence of hypertension [OR=2.83; 95% CI: 1.47-5.45] at Year 16 were independently associated with a higher odds of having reduced SE at follow-up.

Conclusion
These findings suggest that antidepressant use, benzodiazepine use and hypertension are risk factors or markers for the development of reduced sleep efficiency in older women. Future studies are warranted to examine the underlying mechanisms for these associations.

Support
The Study of Osteoporotic Fractures (SOF) is supported by National Institutes of Health funding. The National Institute on Aging (NIA) provides support under the following grant numbers: R01 AG005407, R01 AR35582, R01 AR35583, R01 AR35584, R01 AG005394, R01 AG027574, and R01 AG027576.
0394 Differential Role of Sleep Problems on Depression and Suicide in Community Adolescents
Authors: Kim J , Vander Stoep A , McCauley E 
Published: May 2020

Abstract

Introduction
Sleep changes during adolescence, including “eveningness” or a preference for staying up late, decreased sleep hours, increased daytime sleepiness and irregular sleep patterns, can contribute to adolescent sleep disturbances, such as insomnia, daytime fatigue, and other sleep problems. The goals of the study were; 1) to examine the proportion of adolescents who experienced each type of sleep disturbances based on six sleep problems; and 2) to examine the association between six types of sleep disturbances and concurrent depression, suicide ideation, and suicide attempt at 12th grade.

Methods
Using the data from the Developmental Pathways Project (DPP), a community-based study in adolescence, total 425 students were included. Sequential logistic regression analyses were performed to examine the association between each sleep item and depression, suicide ideation, and lifetime history of suicide attempt.

Results
The most frequently reported sleep problems were ‘overtired without good reason (49.9%)’, ‘sleeps less than most kids (46.8%)’, and ‘sleep more than most kids (46.7%)’. After adjusting for depressive symptoms at baseline, 12th grade reports of ‘overtired without good reason’ (OR = 1.63, 95% CI = 1.22 - 2.17), ‘sleep less’ (OR = 2.03, 95% CI = 1.51 - 2.74), ‘trouble sleeping’ (OR = 1.50 95% CI = 1.10-2.06, p &lt; .05), ‘nightmare’ (OR = 1.51, 95% CI = 1.12-2.02, p &lt; .01) were significantly associated with depression. For suicide ideation (SI) and suicide attempt, ‘nightmare’ (OR = 1.68, 95% CI = 1.15 - 2.48; OR = 2.43 95% CI = 1.30 - 4.53, respectively) was significantly and positively associated with SI and having history of suicide attempt.

Conclusion
‘Nightmare’ has the strongest association with depression, suicide ideation, and suicide attempt. To disentangle the mechanism of the association between nightmares and mental health issues, longitudinal studies examining causal or bidirectional relationships of the mechanism are warranted.

Support
National Institutes of Mental Health and Drug Abuse, Nesholm Family Foundation, Seattle Children’s Hospital Outcomes Research Steering Committee, Loeb Family Foundation, Seattle Children’s Research Institute, University of Washington Office of the Provost, AETNA Foundation.
0397 Neighborhood Physical and Social Environments and Sleep Among Children and Adolescents: A Systematic Review
Authors: Mayne S L , Mitchell J A , Virudachalam S , Williamson A A 
Published: May 2020

Abstract

Introduction
Understanding salient environmental determinants of pediatric sleep is essential for informing interventions and public health initiatives. Emerging evidence suggests the neighborhood environment can impact pediatric sleep. We are conducting a systematic review of studies examining associations of neighborhood physical and social environments with sleep among children and adolescents.

Methods
We searched 6 databases (MEDLINE, PubMed, EMBASE, SCOPUS, Web of Science, PsychInfo) using search terms related to sleep, neighborhood environment, and pediatric populations to identify English-language articles with an abstract. We identified and screened 2,581 abstracts. Inclusion criteria included 1) assessing associations of ≥1 neighborhood-level factor with ≥1 sleep outcome and 2) including participants ≤18 years. We excluded review articles, protocols, qualitative and non-human studies. In total, 134 full-text articles were independently reviewed by 2 reviewers each to confirm eligibility. One reviewer abstracted preliminary data from included studies. Next steps include independent data abstraction by two reviewers using a standardized form, synthesis of results, and assessment of study quality according to the study design, sleep assessment method, sampling strategy, and control for confounding.

Results
Sixty-one articles met inclusion criteria. Fourteen articles included children aged 0-5 years, 38 included children aged 6-12 years, and 36 included adolescents aged 13-18 years (25 included multiple age groups). Twenty-two studies (36%) used objective sleep assessment methods (e.g. actigraphy). Seven studies (11%) examined sleep apnea/snoring. The most common neighborhood-level factors were safety/crime/community violence (n=28) and socioeconomic status (n=25), with fewer studies examining other exposures like noise (n=7) and social cohesion (n=4). Results on key associations and study quality are forthcoming.

Conclusion
A growing body of epidemiological data has emerged in recent years to provide insight into how the neighborhood environment can impact pediatric sleep. Preliminary results suggest few studies have examined associations of the built environment with sleep, with most studies focusing on school-aged children and adolescents.

Support
This work was supported by: the Children’s Hospital of Philadelphia’s Possibilities Project (SLM); Sleep Research Society Foundation and K23HD094905 (AAW); NIH/NHLBI K01HL123612 (JAM)
0435 Autoscoring of Sleep and Associated Events Versus a Reference Scorer Competing with Three Additional Manual Scorings: A Clinical Validation Study
Authors: Anderer P , Ross M , Cerny A , Moreau A 
Published: May 2020

Abstract

Introduction
Manual scoring of polysomnographic (PSG) data is a time-consuming and tedious process with noticeable inter-rater variability. Autoscoring may overcome these limitations if it delivers valid results. The goal of this study was to validate a comprehensive autoscoring system in a clinically representative population.

Methods
The validation data consisted of 97 PSGs in patients with sleep-related breathing disorder, scored manually by a reference scorer and three further experts. The Somnolyzer autoscoring system combined pattern recognition for events such as spindles, k-complexes, slow-waves, eye-movements, apneas, hypopneas, desaturations and leg movements with an artificial intelligence classifier consisting of a bidirectional long short-term memory recurrent neural network (RNN) architecture. Intra-class correlation coefficients (ICC) for absolute agreement were determined for the commonly used metrics in sleep medicine to compare both, the three human expert scorings and the autoscoring versus the reference scoring.

Results
ICC coefficients for autoscoring and the three manual scorings versus the reference scoring were for sleep efficiency: .95, .83, .91, .93; N1(%): .71, .44, .39, .56; N2(%): .87, .63, .55, .45; N3(%): .80, .62, .44, .32; R(%): .92, .89, .91, .88; arousal index: .88, .81, .22, .78; PLMI: .97, .88, .86, .91; AHI: .91, .89, .87, .78; OA: .94, .89, .91, .90; CA: .96, .96, .96, .82; MA: .93, .77, .43, .41. Thus, the ICCs between autoscoring and the reference scoring were equal or higher than the ICCs between any of the three manual scorings and the reference scoring for all endpoints.

Conclusion
Autoscoring of PSGs based on artificial intelligence outperformed even the best of three human expert scorers. Since the autoscoring performs pattern recognition in real-time, the final autoscoring results are available immediately after the end of the recording.

Support
All authors are employees of Philips
0436 Deep Learning for Scoring Sleep Based on Signals Available in Home Sleep Apnea Test Studies: Cardiorespiratory Sleep Staging
Authors: Anderer P , Ross M , Cerny A , Radha M , Fonseca P 
Published: May 2020

Abstract

Introduction
Typically, neurological signals are not recorded in home sleep apnea testing (HSAT) and thus standard sleep scoring is not applicable. The respiratory event index is calculated using total recording time rather than total sleep time (TST) resulting in a risk of underestimating sleep apnea severity. The objective of the study was to evaluate if artificial intelligence approaches can provide sleep scoring based on cardiorespiratory signals (CReSS) with reasonable accuracy.

Methods
Supervised deep learning for scoring sleep was trained with 472 and tested in 116 polysomnographies (PSG), scored independently by two experts and by a consensus scorer. The resulting bidirectional long short-term memory recurrent neural network (RNN) was integrated in the Somnolyzer system and validated in 97 PSGs of patients with obstructive sleep apnea (OSA) which had been scored independently by four human experts. Cohen’s kappa agreement for four stages (W, L: N1+N2, D: N3, R) was determined as compared to a consensus scoring.

Results
Epoch-by-epoch comparison between CReSS autoscoring and manual consensus scoring resulted in Cohen’s kappa of 0.68 (W: 0.74, L: 0.63, D: 0.54, R: 0.79). The intra-class correlation coefficient (ICC) between TST derived from CReSS and from neurological scoring was 0.86 (95%-CI: 0.79-0.90), while the ICC between subjective TST from sleep questionnaire and the objective TST was only 0.65 (95%-CI: 0.45-0.77). REM-related OSA had a prevalence of 16% and was detected with an accuracy of 95%.

Conclusion
With a kappa of 0.68, the cardiorespiratory-based RNN classifier is far above previously published values and reflects a substantial agreement with the manual consensus scoring in patients with sleep-disordered breathing. Thus, applying CReSS allows a more accurate determination of the OSA-severity and even a detection of REM related OSA in HSAT studies.

Support
All authors are employees of Philips
0439 Nonlinear Dynamics Forecasting for Personalize Prognosis of Obstructive Sleep Apnea Onsets
Authors: Le T 
Published: May 2020

Abstract

Introduction
The emphasis on disease prevention, early detection, and preventive treatments will revolutionize the way sleep clinicians evaluate their patients. Obstructive Sleep Apnea (OSA) is one of the most prevalent sleep disorders with approximately 100 millions patients been diagnosed worldwide. The effectiveness of sleep disorder therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes.

Methods
This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. The method includes (a) a representation of a transition state space network to characterize dynamic transition of apneic states (b) a Dirichlet-Process Mixture-Gaussian-Process prognostic method for estimating the distribution of the time estimate the remaining time until the onset of an impending OSA episode by considering the stochastic evolution of the normal states to an anomalous (apnea)

Results
The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of eight subjects from previous work. The average prediction accuracy (R2) is reported as 0.75%, with 87% of observations within the 95% confidence interval. Estimated risk indicators at 1 to 3 min till apnea onset are reported as 85.8 %, 80.2 %, and 75.5 %, respectively.

Conclusion
The present prognosis approach can be integrated with wearable devices to facilitate individualized treatments and timely prevention therapies.

Support
N/A
0440 Validation Study of Neural Network Algorithm for Automated Sleep Stage Scoring: StageNet
Authors: Choi J , Moon J 
Published: May 2020

Abstract

Introduction
Polysomnography (level 1) is an important test for evaluating sleep status or disorder. In general, measured raw data of polysomnography is manually scored by sleep expert. However, manual scoring is a time-consuming and labor-intensive work. The purpose of this study was to verify the accuracy of automated sleep stage scoring based on the neural network algorithm compared to the manual sleep stage scoring.

Methods
A total 604 polysomnography data set of subjects (Male: Female = 409: 195) aged 19 to 60 years were finally included in the study. The performance of proposed model was evaluated with kappa and bootstrapped point-estimated of median percent agreement with 95% percentile bootstrap confidence interval with R=1000. The proposed model is trained with 484 data set and validated with 48 data set. For test, 72 data set are randomly selected.

Results
The proposed model showed good concordance rates in stage W (94%), N1 (83.9%), N2 (89%), N3 (92%) and R (93%) between automated neural network algorithm and manual scoring. The average kappa value was 0.85. In bootstrap method, high overall agreements of automated neural network algorithm were found in stage W (98%), N1 (94%), N2 (92%), N3 (99%), R (98%) and total (96%).

Conclusion
Automated sleep stage scoring using proposed model - StageNet may be a reliable method for sleep stage classification.

Support
0449 Characterizing Spindle Activity as a Time-Frequency Phenomenon
Authors: Dimitrov T S , He M , Prerau M J 
Published: May 2020

Abstract

Introduction
Spindles are currently defined clinically based on observed patterns in the EEG waveform trace, with automated methods seeking to replicate visual scoring by experts. Recent work suggests that sleep spindles may be more readily observed as time-frequency peaks in the EEG spectrogram. This study compares spectral peaks in the multitaper spectrogram to expert and automatic detection scoring, characterizes the variability of spindles across a night, and investigates topographical and temporal clustering of spindles within individual EEG records.

Methods
We compared spectral peaks, expert scoring, and automatic detection in two datasets (DREAMS, and a high-density control study). Peaks were identified using multitaper spectral estimation and the peak prominence of the normalized power spectrum for each channel. Spatiotemporal variability analysis was performed using cluster and pattern recognition algorithms including penalized sorting of channel activation order, 2D-cross correlation, PCA and UMAP cluster analysis, and the seqNMF method.

Results
Spectral peaks were shown to be highly robust to and easily differentiated from broadband noise, occuring at rates (10-16 per min) far exceeding spindle rates reported in literature (~2.5 per min). Expert scoring and automated scoring failed to capture clear spectral peaks in the time-frequency domain, indicating an underreporting of the phenomenology. No apparent clustering or patterns of sleep spindle-like activity was observed using the proposed methods, suggesting high variability of spatiotemporal evolution of spindles.

Conclusion
These results suggest that the difficulty of time-domain visual scoring of spindles causes an artificially low estimate of the underlying phenomenology, which is mirrored in the assumptions implicit in the thresholds of automated scorers. This work shows that spindles are highly variable in their spatiotemporal evolution, suggesting that there is no optimal single electrode for analysis and casting doubt on the presence of a single cortical generation mechanism. We must therefore revisit the concept of the spindle using the time-frequency domain to more robustly characterize underlying phenomenology.

Support
National Institute Of Neurological Disorders And Stroke Grant R01 NS-096177
0451 Fully Automatic Detection of Sleep Disordered Breathing Events
Authors: Thybo J , Olesen A N , Olsen M , Leary E , Arnal P , Sørensen H B , Jennum P , Mignot E 
Published: May 2020

Abstract

Introduction
Evaluation of sleep apnea involves manual annotation of Polysomnography (PSG) file, a time-consuming process subject to interscorer variations. The DOSED algorithm has been shown to be helpful in detecting Central Sleep Apnea (CSA), Obstructive Sleep Apnea (OSA), and Hypopnea when merged into a single event type. This work uses a modified version of DOSED capable of detecting each event type separately.

Methods
The network consists of 3 blocks of 1D convolutional layers followed by 6 blocks of 2D convolutional layers. The network has 2 classification layers, one determines the probability of each class, and the other determines the start and duration time of the event with the highest probability. Four channels from nasal and mouth airflow and position of abdomen and thorax are used as input to the model. The model was trained using 2800 PSG from 4 different cohorts (MESA, MROS, SSC, WSC) and tested on 70 PSG, which have been scored by six technicians (Stanford, U Penn, St Louis).

Results
On an event by event basis, model F1-scores versus a weighted consensus score based on 6 technicians were 0.60 for OSA, 0.43 for CSA, and 0.34 for Hypopnea. Average F1-scores for the 6 technicians were 0.48 (std 0.04) for OSA, 0.29 (std 0.145) for CSA, and 0.54 (std 0.183) for Hypopnea, indicating that the model functions better on an event-by-event basis than an average technician. Correlations between indices/hr for central apnea, obstructive apnea, and hypopnea indicate excellent correlations for apneas, but poor correlation for hypopnea. We are now adding the snoring channel to explore if predictions can be improved.

Conclusion
The result shows that deep learning-based models can detect respiratory events with an accuracy similar to technicians. The poor agreement between technicians from different universities indicates that we need better definitions of hypopnea.

Support