bluuebunny/crossref_metadata_2025_split huggingface dataset:
0111 Nighttime Vagal Nerve Stimulation Interacts with Sleep to Predict Next-day Affective States
Authors: Silva-Jones Jillian , Subramoniam Anjana , Crosswell Alexandra , Whitehurst Lauren 
Published: April 2024

Abstract

Introduction
Sleep and the autonomic nervous system (ANS) independently modulate emotional experiences and interact bidirectionally - changes in ANS modulate sleep and stages of sleep are associated with distinct autonomic profiles. Changes to ANS via transcutaneous vagal nerve stimulation (tVNS) can improve emotion recognition and mood, however, the role of sleep has not been considered. We used a 14-day within-subject, sham-controlled study to examine the impact of nightly tVNS administration on sleep and morning affect.

Methods
Participants(N=20participants, aged=18+) received seven continuous days of non-invasive, active or sham (counterbalanced) tVNS via the left cymba concha between 9PM-11PM each night across two weeks in naturalistic sleeping conditions. Participants completed morning and nightly affect diaries and wore an actigraph watch to track sleep/wake behavior (N=136observations). Affect was aggregated into high-arousal/negative (distressed, irritable), low-arousal/negative (nervous, on edge), high-arousal/positive (alert, attentive, happy), and low-arousal/positive (content, safe). Actigraphy determined total sleep duration, wake minutes in bed, sleep onset latency(SOL), and sleep continuity or wake after sleep onset(WASO).

Results
Linear mixed effects models examined how night affect, stimulation condition, and sleep behavior predicted next morning affect. A three-way-interaction emerged: low-arousal/negative affect pre-sleep predicted low-arousal/negative affect post-sleep on nights when people experienced longer SOLs and active stimulation (ß=-.40, p=.002). Importantly, on nights with shorter SOL, active stimulation led to reductions in low-arousal/negative affect the next morning (ß=-.63, p=.02). Another three-way-interaction emerged such that low-arousal/negative affect, active stimulation, and WASO at night predicted low-arousal/negative affect in the morning. Nights with less WASO and sham stimulation predicted mornings with low-arousal/negative affect (ß=1.26, p=.03). Also, a three-way interaction suggested that nights with high-arousal/negative affect, more WASO, and sham stimulation, also led to mornings with high-arousal/negative affect. However, nights with high WASO and active stimulation led to mornings with the highest amount of high-arousal/negative affect (ß=.89, p=.003). Lastly, low-arousal/positive affect at night led to morning increases in the same emotions, but active stimulation blunted this effect (ß=-.58, p=.04).

Conclusion
Findings suggest that sleep and tVNS are influential in mitigating or intensifying the impact of nighttime affective states on morning affect, particularly for negative emotions. TVNS’s impact on noradrenergic signaling may account for these findings.

Support (if any)
1110 Utilizing Consumer Wearables to Empower and Engage Patients Prescribed non-PAP Obstructive Sleep Apnea Therapy
Authors: Chohan Moeed , Mak Selene , Ash Garrett , Ghadimi Sara , Der-Mcleod Erin , Naeem Saadia , Moore Virginia , Zeidler Michelle , Fung Constance H 
Published: April 2024

Abstract

Introduction
Increased adoption of consumer wearables has resulted in abundant patient-generated health data (PGHD). However, guidance on incorporation of metrics from such devices in the management of sleep disorders is lacking. We aimed to develop a PGHD program, using continuous oxygen saturation (SpO2) monitoring from a consumer wearable as a prototype. By implementing SpO2 data and patient survey outcomes in reports, we engaged patients prescribed non-PAP therapy for obstructive sleep apnea (OSA) and promoted shared decision making to enhance therapy.

Methods
In an ongoing quality improvement project at a VA medical center, we conducted three plan-do-study-act (PDSA) cycles. Patients share SpO2 data collected with a clinic-issued Fitbit via a third-party platform (Fitabase), OSA patient-reported outcome (PRO) measures and their nightly use of non-PAP therapy via Qualtrics. We created a report that juxtaposes SpO2 distribution, PRO measures, and therapy compliance and then send it to the patient for review. At cycle end, patients meet with a sleep clinician to discuss the reports, OSA therapy, and program feedback.

Results
Eleven patients aged 30 to 85 have enrolled (3-5 per cycle). In Cycle 1 (7 days), we developed orientation procedures and patient-facing materials and refined the process for merging the SpO2, PRO, and patient-reported therapy use into a graphical and textual report. A majority of patients (67%, n=6 of 9) indicated motivation to retrial PAP therapy after seeing low SpO2 values. We developed a standardized script to clearly explain the report and data for our patients. In Cycle 2 (14 days), we tested the standardized script and troubleshot barriers to accessing data that emerged— ie unexplained gaps in SpO2 values and device sync issues. In Cycle 3 (28 days), we revised the graphs to reduce production time. Eight patients completed the post program survey. When asked if they would recommend the program (scale 1-10; 10 best), the average score was 9.25.

Conclusion
We developed a consumer wearable based program to engage patients in OSA treatment. Incorporating oxygen data from wearables may promote self-management and participation in shared decision making, which ultimately may improve health outcomes of OSA.

Support (if any)
VA Office of Connected Care
1111 Association Between Technology Use and Sleep: Examination of Population Data from National Sleep Foundation
Authors: Sparks Christopher , Carlson Steven , Dzierzewski Joseph , Baron Kelly 
Published: April 2024

Abstract

Introduction
Evening technology use is common and may be associated with poorer sleep health. The purpose of this study was to evaluate rates of technology before bedtime in a national sample and examine the association between technology use and sleep quality and duration.

Methods
We evaluated sleep and technology use among a nationally representative sample of US adults collected by the National Sleep Foundation. Participants reported their use of four classes of technology in the hour before bedtime (i.e., A) TV, smart TV, game consol, B) smartphones, C) computers, laptops, tablets, D) other devices) and where they used these devices (i.e., in the bedroom, outside of the bedroom, both in and outside the bedroom). Participants also reported their average sleep duration over the past week on both weekdays and weekends. Sleep quality was rated on a 5-point scale using a single item from the Sleep Health Index. We used weighted analyses to evaluate descriptives, as well as associations between technology use, location of technology use, and sleep duration and quality using t-tests and regression analysis while controlling for age and gender.

Results
Data were available for 1,009 participants (Mage = 48.2, SD = 18.0, 51.3% women). Results indicated that 90% of participants used electronic devices within one hour of bedtime, with 76.6% used electronic devices in the bedroom. The most frequently used devices before bed were TVs (71.3% of the sample and among TV watchers 49.7% used in the bedroom) and smartphones (70.6% of the sample and among smartphone users, 79.1% used in the bedroom) Individuals who used smartphones before bed had poorer sleep quality compared to those who did not use a smartphone (t(1007) = -2.19, p = 0.029). There were no other significant associations between number of devices, specific device use, or device use location with sleep duration or quality.

Conclusion
Technology use before bed and in the bedroom is a common occurrence, and smartphone use in the hour before bed was associated with poorer sleep quality. Given the widespread use of smartphones before bed, reducing use in the hour before bed may have implications for improving sleep health.

Support (if any)
1112 Performance Evaluation of a Multisensor Ring During a Clinical Multiple Sleep Latency Test
Authors: Amadei Michael , Freckleton Dustin , Kennedy Kathryn E R , Grandner Michael 
Published: April 2024

Abstract

Introduction
Commercially-available wearable devices are increasingly used to detect sleep-wake patterns in free-living conditions. These devices, however, are infrequently assessed relative to their ability to detect naps. The present study evaluated whether a commercially-available wearable device was able to detect nap onset during a clinical Multiple Sleep Latency Test (MSLT).

Methods
Participants referred for a clinical MSLT wore a commercially-available multisensor ring device (Happy Ring; Happy Health) during their in-lab assessment at one of two AASM-accredited sleep centers. Each sleep lab followed AASM guidelines for administering the MSLT and scoring the polysomnogram for sleep onset. Due to potential differences in scoring across sites, a third reviewer (INSERT CREDENTIALS HERE) blindly scored all records, also according to AASM guidelines.

Results
Internal sleep lab scoring somewhat differed from the independent scorer, with 90% agreement regarding the proportion of naps that were clinically significant; there were 2 cases where only the sleep lab reported a clinically significant MSLT and 3 cases where only the independent rater reported a clinically significant MSLT. The ring achieved 86% diagnostic accuracy relative to the lab scorer and 84% relative to the independent scorer. The mean difference between the lab and independent scorer for sleep latency was 142 seconds, with N=30 records that achieved agreement of &lt; 150 seconds. Of these 30 records, the Happy Ring achieved 96.7% accuracy (only 1 case of disagreement). The mean difference between the lab scorer and the ring was 152 seconds.

Conclusion
The Happy Ring shows promise in its ability to detect sleep onset during an MSLT. Future work could be used to optimize wearables for being able to predict who will test positive in an MSLT, thus reducing costs, wait times, and patient burden.

Support (if any)
Happy Health
1115 Nano-material Based Brain Computer Interface (BCI) Sleep Monitoring Device and Its Application
Authors: Sun Yu 
Published: April 2024

Abstract

Introduction
PSG (Polysomnography) as a traditional instrument for multi-modal monitoring device, has been considered as golden standard for sleep related vital sign monitoring for many decades. But it is sometimes considered burdensome due to its wet EEG electrodes cap, its time-consuming wearing, and it is not suitable for tele-health and at-home monitoring purpose, for chronic disease management especially for elderly senior people.

Methods
Given this, a nano-electronic based sleep monitoring patch device has been developed to overcome these obstacles. The device implements metallic nano-particle electrodes to increase data acquisition efficiency, and well-developed hydrogel to increase skin biocompatibility and gas permeability. Also, the device integrates EEG chip design and three-electrode based setup, to avoid traditional 8-channel or 16-channel EEG headset and dramatically increase the convenience of usage. In terms of software, the device uses artificial intelligence deep learning network to train sleep staging algorithm and implement advanced data processing technology to differentiate EEG, EOG and EMG from the raw data acquired by the device in the forehead.

Results
In this way, the device can also detect characteristic waveform such as slow waveform, kappa complex and spindle etc. All these factors result in 85-95% accuracy of sleep staging percentages, as compared to PSG golden standard, which is dramatic.

Conclusion
The device has passed Class II medical NMPA approval in China and it is getting approval in US FDA very soon. It is believed that this device can be used not only in traditional sleep center and hospital all over the world, but can it also be used in a variety of different departments such as psychiatry, ICU and neurology, as well as rehab centers, nursery, and many other circumstances and locations. It is believed it is beneficial not only for doctors, but for researchers as well.

Support (if any)
1119 Validation of a Portable Sleep Electroencephalography Device in Good Sleepers and People with Sleep Apnea
Authors: Lanthier Malika , Foti Micheal-Christopher , Higginson Caitlin , Tavakoli Paniz , Oksit Defne , Ray Laura , Fogel Stuart , Robillard Rebecca 
Published: April 2024

Abstract

Introduction
Sleep wearable restricted to accelerometry or heart rate monitoring have proved to be helpful to delineate sleep-wake profiles outside of the laboratory environment, but have limited accuracy and only provide indirect estimations of sleep and wake states. This study aimed to assess the validity of a novel electroencephalography headband for ambulatory sleep monitoring as compared to standard polysomnography in good sleepers and individuals with sleep apnea.

Methods
Forty-seven adult males and females from the community took part to this study. This includes a preliminary sample of eight individuals with sleep apnea detected through level 1 polysomnography. All participants underwent one night of in-laboratory sleep recording with the portable EEG headband (MUSE-S, Interaxon) and simultaneous standard polysomnography (Embla N7000/RemLogic, Natus). The Muse-S headband is a commercially available consumer headband with 7 EEG sensors: 2 on the forehead, 2 behind the ears, and 3 reference sensors. MUSE-S data was scored using an automated sleep staging algorithm. Polysomnography data was scored by an independent registered technologist who was blinded to the MUSE-S algorithm-based scoring.

Results
In the overall sample, the accuracy of the Muse-S relative to standard polysomnography ranged between 88% and 96% across all sleep stages, with a sensitivity of 79% to 92%, and a specificity of 90% to 99%. Cohen’s Kappa for all sleep stages combined was 0.76 (CI:0.75-0.76). Analyses per sleep stages showed that Cohen’s Kappa scores were in the fair agreement range for NREM 1 sleep (K=0.41, CI:0.39-0.43), increased to the substantial agreement range for both NREM 2 (K=0.75, CI:0.74-0.75) and NREM3 sleep (K=0.77, CI:0.76-0.77), and further increased to the near perfect agreement range for REM sleep (K=0.85, CI:0.85-0.86) and wake (K=0.84, CI: 0.83-0.84). Similar results were obtained in the subgroup with sleep apnea (overall K= 0.87, CI:0.85-0.88; NREM 1 K=0.33, CI:0.28-0.38; NREM 2 K=0.72, CI:0.70-0.73; NREM3 K=0.81, CI:0.79-0.79; REM K=0.80, CI:0.78-0.82) and wake (K=0.86, CI:0.85-0.88).

Conclusion
Portable EEG-based sleep monitoring with the MUSE-S shows good validity for sleep macroarchitecture variables relative to standard polysomnography. Fair to near perfect concordance was observed across sleep stages in a diverse sample of good sleepers and people with sleep disorders.

Support (if any)
1121 Multi-sensor Fusion Approach for Sleep Stage Analysis: A Non-contact Sensor Study
Authors: Kim Doyoon , Chen Hao , Lee Joonhyun , An Yuhyun , Chang Hao Hsuan , Kim Seho , Park Jonghyun , Yang Seungman , Bae Hyoeun , Joo Eunyeon 
Published: April 2024

Abstract

Introduction
There has been a growing interest in utilizing non-contact sensors such as photoplethysmogram (PPG), ultra-wideband (UWB), millimeter-wave (mmWave) or piezoelectric sensors to measure physiological signals, which are then processed through machine learning for sleep stage analysis. In this study, we conducted an analysis of sleep stages by leveraging data collected from multiple sensors on the subjects and employing machine learning algorithms to combine and interpret the information.

Methods
mmWave and UWB sensors were placed within approximately 500mm around the subject, and Piezo sensors were positioned inside the subject's mattress. Concurrently, physiological signals from those sensors and from polysomnography (PSG) were obtained. . Key indicators for machine learning were extracted from the sensor data collected from each sensor. Convolutional neural network (CNN) and Transformer machine learning methods were employed to derive sleep stages for each sensor. the sleep stages obtained from each sensor were used as indicators, combined to estimate the final sleep stage. The results were then compared and analyzed against PSG parameters to estimate the accuracy of the algorithm.

Results
A total of 340 participants (244 males, mean age 44.95± 12.40 y) were enrolled in this study. Data from 200 individuals were utilized to train sleep estimation algorithms for each sensor, while data from 110 individuals were employed to train algorithms that combine results from each sensor to determine sleep. Additional 30 datasets were used to validate these algorithms. The sleep stage accuracy (Accuracy = True epoch/ Total epoch) relative to PSG: mmWave (72.9%), UWB (75.5%), and Piezo (72.4%). Cohen’s Kappa values were mmWave (0.55), UWB (0.62), and Piezo (0.53). The fusion algorithm based on each sensor demonstrated an accuracy of 79.8% and a Cohen’s Kappa value of 0.69 compared to PSG.

Conclusion
In contrast to previous research relying on single-sensor predictions, our approach harnessed the diverse strengths of different sensor types, conducting a comprehensive analysis of sleep stages by combining data collected from multiple sensors.

Support (if any)
1122 A Deep Learning Model for Inferring Sleep Stage from a Flexible Wireless Dual Sensor Wearable System Without EEG
Authors: Zhang Andrew , Li Chunlin , Tang Yuzhi , He-Mo Alex , Ghahjaverestan Nasim Montazeri , Goubran Maged , Lim Andrew 
Published: April 2024

Abstract

Introduction
In-lab polysomnography (PSG) is costly and difficult to scale due to a need for specialized personnel for data acquisition and annotation. Numerous novel wearable devices without electroencephalography (EEG) have been developed to improve scalability of data acquisition. However, validated automated approaches to data annotation, including sleep staging are needed. Here, we apply deep learning approaches to the problem of sleep staging using data from the ANNE One (Sibel Health, Evanston, IL), a minimally intrusive flexible wireless dual sensor system measuring chest electrocardiography (ECG), triaxial accelerometry, and temperature, and finger photoplethysmography (PPG).

Methods
We obtained wearable sensor recordings from 281 adults undergoing concurrent clinical polysomnography at a tertiary care sleep lab. PSG recordings were scored according to AASM criteria. PSG and wearable sensor data were automatically aligned using their ECG signals with alignment confirmed by visual inspection. We trained a neural-network model to predict both 3-class (Wake, NREM, REM) and 2-class (Wake, Sleep) sleep stage classifications using a randomly selected 85% of the recordings and tested the model on the remaining recordings. We applied the model to ambulatory wearable sensor recordings from 233 older adults at risk for dementia. Our neural-network employed a convolutional-encoder and autoregressive-decoder architecture. In addition to time domain signals, we also engineered frequency domain features as well as selected scalar and metadata features as input to our model to improve performance. Ensembling of model variants was performed.

Results
Our approach achieved a 2-class macro-F1 of 0.718 with a sensitivity of 0.760 and specificity of 0.763 and a 3-class macro-F1 of 0.585 (wake precision 0.564 accuracy 0.745; NREM precision 0.886 accuracy 0.634; REM precision 0.258 accuracy 0.671). Our feature engineering and training techniques offered a 9% performance improvement from the time domain signals only baseline given the same neural network architecture, while ensembling different model variants offered a further 4% performance improvement.

Conclusion
A deep learning model can infer sleep stage from an EEG-less flexible wireless system and can be successfully applied to data from older community-dwelling adults at high risk for dementia.

Support (if any)
The Centre for Aging and Brain Health Innovation, Canadian Institutes of Health Research, National Institute on Aging
1123 Circadian Phase Assessments from Clinical Patients Using an At-Home Self-Collection Saliva Kit
Authors: Okun Michele , Schwartz Chris , Granger Steve 
Published: April 2024

Abstract

Introduction
The dim light melatonin onset (DLMO) is the gold-standard for estimating sleep timing and is recognized as a reliable diagnostic tool for circadian rhythm sleep-wake disorders (CRSWD). Its assessment may help optimize treatment of various sleep disorders. The DLMO is usually assessed in a research laboratory or clinic, which presents significant barriers. Thus, there is a need to further evaluate the ecological validity of self-directed collection at home. Salimetrics, Inc. recently introduced a circadian phase assessment kit that allows for self-collection of saliva at home accompanied by laboratory assessed DLMOs. This is a preliminary report of a limited dataset identifying normal (typical) and abnormal (atypical) melatonin profiles.

Methods
Sleep clinic patients (N = 197) utilized one of four different protocols that varied in the number and timing of saliva collection based on their provider’s suggestion. Despite different protocols, the actual sampling protocol was performed under the same guidance for all assessments. A typical profile was determined as an expected onset (estimated ~2.5-1.5 hours before bedtime) with a baseline of &lt; 10 pg/ml and a sleep onset of &gt;10 pg/ml. An atypical profile fell outside these parameters. Routine medication usage was also analyzed to determine if there was a correlation between melatonin modulating medications and resulting profiles. Only sex and age were recorded as the participants collected samples as part of the clinical care protocol.

Results
Findings indicate 22% typical profiles vs. 78% atypical profiles. With regards to medication use: 26% of typical profiles vs 19% of atypical profiles had no medications listed, 53% vs 68% used a melatonin modulator, respectively, 14% vs 54% used a psychiatric medication, 21% vs 25% used 2+ psychiatric medications, and 9% vs 14% used a melatonin supplement within 10 days of assessment.

Conclusion
These data corroborate earlier investigations by Burgess and colleagues and suggest that the use of a self-directed, at-home DLMO assessment is feasible and accurate. It further suggests that medication use, or an underlying psychiatric disorder can modulate melatonin secretion exaggerating the CRSWD. Data indicates that this diagnostic tool can accurately identify many clinical sleep patients with an existing CRSWD or significant sleep wake misalignment.

Support (if any)
Salimetrics