bluuebunny/crossref_metadata_2025_split huggingface dataset:
0511 High Frequency Radar for Non-Contact Detection of Respiratory Signals During Sleep
Authors: Goswami Umesh , Kablan John , Condon John 
Published: April 2024

Abstract

Introduction
Polysomnography (PSG) requires on-body sensors for respiratory effort measurement, that can adversely affect sleep quality and accuracy of detection of sleep related breathing disorders (SRBD). We developed a non-contact 60 Hz pulsed wave radar system with integrated advanced signal processing to collect respiratory effort, heart rate and gross body movements data using phase differencing to a sub-mm resolution. In this study we tested the performance of this system in detecting SRBD events against standard PSG in human participants.

Methods
During participants’ clinically indicated PSG, we collected concurrent data from the radar system placed under the bed on a tripod mount. Algorithms were developed to quantify breath amplitude reductions collected by radar and compared to the time-synced signals derived from respiratory impedance plethysmography belts. A Convolutional Neural Network (CNN) was trained on radar in-phase/quadrature (I/Q) data for each 30 second epoch to output probability of apnea and hypopnea events using standard event definitions. Receiver Operating Characteristics (ROC) plot and confusion matrix for the trained network were generated to provide accuracy of event predictions.

Results
A total of 60 participants completed the study. The participants had a mean age of 65 years (Inter Quartile Range 13.5 years), and 56% subjects were male. The mean AHI for the entire cohort was 12.9 (IQR 14.75) events/hour. ROC plots showed an Area Under the Curve (AUC) of 0.98 for events detection by radar compared to human scored PSGs. The confusion matrix was generated at an operating point of 95% sensitivity and 80% specificity.

Conclusion
The non-contact high frequency radar system showed excellent performance in respiratory event detection in patients with sleep-related breathing disorders in comparison to the gold standard PSG system. Use of this technology is expected to reduce the number of body contact sensors used during conventional sleep studies and thus improve patient comfort and accuracy of results.

Support (if any)
Research reported in this abstract was supported by National Institute of Aging of the NIH under award number: R44AG060779
0513 Assessment of Sleep Fragmentation in a Large US Sample by Home Under-Mattress Devices
Authors: Kushida Clete , Cotton-Clay Andrew , Baron Susan , Fava Laura , Wray Andrew , Easwar Venkat , Kinsolving Arthur , Leng Yue , Zitser Jennifer , Kahn Philippe 
Published: April 2024

Abstract

Introduction
Sleep fragmentation by arousals and sleep-disordered breathing (SDB) is known to increase with age and differ by gender, although the magnitude of these effects is largely unknown. Population studies allow large collection of continuous nightly data to explore these effects.

Methods
Sleep fragmentation index (SFI, arousals/hour) and SDB were analyzed by a commercially available home monitoring device (Sleeptracker-AI Monitor, Fullpower Technologies Inc., California, USA) that passively monitors sleep using piezo-electric sensors. Validated sleep/respiratory parameters were derived from device data. De-identified data were analyzed, following review and exemption of the study (#57681) from Stanford University IRB. SFI was assessed using an estimated arousal index, estimating individual arousal events from passive sensor data, with validation against PSG. Data 8/16/2023-12/15/2023 were reviewed. Individuals with birth year and gender provided and age 20-79 years in 2023 were included in the analytic dataset.

Results
117,846 individuals (64,049 men, 48.2±12.6 years; 53,797 women, 48.3±12.6 years) with 10,090,778 recorded nights met the inclusion criteria. For AHI&lt; 5, the SFI mean±SE for women and men were 13.6±0.16 and 12.6±0.13 at 20-24 years, respectively, and increased with age to 15.0±0.26 and 16.0±0.24 for 75-79 years. For AHI&lt; 5, men showed a significantly greater SFI during REM sleep than women across all ages above 24 years, but not during NREM sleep. For AHI&lt; 5, SFI significantly increased with age during NREM sleep (women 11.9±0.15 to 14.6±0.17, men 10.8±0.12 to 15.1±0.26), but for REM sleep increased up to 25-34 years (women 18.8±0.12, men 19.6±0.09), but then steadily decreased with age to 75-79 years (women 16.1±0.27, men 18.1±0.24), and a similar trend was found for AHI≥5. For AHI≥5 compared to AHI&lt; 5, SFI was significantly higher by 6-13 arousals/hour across all ages.

Conclusion
Use of a noninvasive in-home monitoring device enabling continuous nightly collection/analysis of a large sample of sleep/respiratory data revealed differences in sleep fragmentation across age and by gender. Greater sleep fragmentation was observed for men across age during REM sleep, and fragmentation during REM sleep decreased with age after 34 years, which is unexpected and warrants further study given that overall quantities of NREM and REM sleep decrease with age.

Support (if any)
Fullpower
0514 Can the PPG Cardiac Component Envelope Reflect the Respiratory Drive?
Authors: Hsu Chih-Wei , Wu Hau Tieng 
Published: April 2024

Abstract

Introduction
Using a home-based fingertip monitor can decrease the time and cost of screening obstructive sleep apnea (OSA). During OSA events, respiration is partially or totally blocked, which stimulates the compensatory respiratory drive. We discovered a significant cardiac component envelope dynamics induced by the apnea events, particularly a strong surge following most apnea events. We conjecture that it reflects the respiratory drive.

Methods
We recruited 76 participants with suspected OSA. Each participant attends overnight PSG analysis with the TipTraQ device, which is installed in the fingertip with red, infrared and green photoplethysmography (PPG). Sleep apnea events were labeled by the same sleep experts. The envelope of the cardiac component (EC) and the instantaneous heart rate (IHR) were extracted from each PPG channel. The surge in the EC and IHR near the end of each OSA event was extracted by the spectrum analysis. The lag seconds of the surge after each event, latency between the EC surge and the IHR surge, and maximal shifted correlation coefficient were evaluated.

Results
During periods of frequent OSA, the EC surge and the IHR surge are well-aligned. The correlation coefficient is over 0.8 with the EC surge shift of about 4 seconds forward. The lag seconds are distributed from 10 to 40 for both channels’ surges, central at 25. The age does not affect distributions.

Conclusion
The study results show that after OSA events, the EC surges often happen after IHR surges. This suggests that the sympathetic stimulation induced by OSA happens earlier than hemodynamic changes reflecting respiratory efforts, especially when participants enter the frequent OSA periods state, which further suggests that EC encodes respiratory drive. This observation could help better determine whether a patient is suffering a severe apnea from the mobile fingertip devices like TipTraQ.

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0517 Integrating Body Sensor into a Wearable Platform to Enhance the Identification of Central and Mixed Apneas
Authors: Gu Wenbo , Wu Peter , Liu Arthur , Liu Wen-Te , Kuan Yi-Chun , Lee Hsin-Chien , Leung Lydia , Wu I-Chen , Chiang Ambrose 
Published: April 2024

Abstract

Introduction
Accurate identification of apnea types is crucial for effective diagnosis and management of sleep-disordered breathing. The Belun Sleep System (BLS-100, a.k.a., Belun Ring) is an FDA-cleared home sleep apnea testing system (K222579) comprising an adjustable ring-shaped wearable, a cradle, and two deep learning-powered algorithms. The Belun Cor, a novel subxiphoid sensor equipped with accelerometry, can detect respiratory effort, respiratory rate, body position, and facilitates the detection of central events. This preliminary analysis aims to assess the performance of the integrated BLS-100 in detecting apnea events containing central components.

Methods
This interim analysis evaluated the performance of BLS-100 in a clinical cohort of hospitalized patients admitted for acute ischemic stroke. Eligible patients underwent in-lab polysomnography (PSG) alongside concurrent BLS-100 testing. PSG scoring adhered to the latest AASM scoring manual, with scoring technicians blinded to the BLS-100 results. The BLS-100 derived total sleep time (bTST), sleep stages (bSTAGE), apnea-hypopnea index (bAHI), and combined central and mixed apnea index (bCMAI).

Results
As of 12/17/2023, 25 consecutive Taiwanese patients were enrolled. Four patients were excluded due to short bTST&lt; 120 mins. The analysis was conducted on 21 patients. M:F 19:2; age 59.7; PSG TST 270 ± 61.9 mins; PSG AHI 27.0 (1.4-81.9) with 3 normal, 3 mild, 7 moderate, and 8 severe OSA cases. The mean PSG central apnea index (PSG-CAI) was 4.8 (0.0-34.0), with 5 patients having PSG-CAI≥5. The mean PSG central and mixed apnea index (PSG-CMAI) was 8.4 (0.0-47.3). Pearson correlation coefficients between PSG-CAI and bCMAI, as well as PSG-CMAI and bCMAI, were 0.939 (P&lt; 0.001) and 0.982 (P&lt; 0.001), respectively. Using bCMAI≥5 to predict PSG-CAI≥5, the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Cohen’s Kappa were 0.81, 1.00, 0.75, 0.56, 1.00, and 0.59, respectively. Similarly, using bCMAI≥5 to predict PSG-CMAI≥5, the corresponding values were 0.86, 0.88, 0.85, 0.78, 0.92, and 0.70, respectively.

Conclusion
Early findings indicate that the BLS-100 with Belun Cor shows promising performance in identifying apnea events that include central components. An elevated bCMAI serves as a valuable indicator for clinicians, signaling the presence of central or mixed apneas.

Support (if any)
Case Western Reserve University-Taipei Medical University Pilot Award
0518 Longitudinal Testing with a Home Sleep Apnea Test Shows Large Variability in Nightly Sleep Apnea Severity
Authors: Rohrscheib Chelsie 
Published: April 2024

Abstract

Introduction
Sleep apnea (SA) is traditionally diagnosed by a single-night sleep study, however research suggest that nightly variability in SA severity is common. The purpose of this study was to utilize a novel longitudinal home sleep apnea test (HSAT) to evaluate the extent of variability and identify the ideal number of tests for diagnostic accuracy.

Methods
This study retrospectively evaluated 149 adults across the U.S. that used a novel type-3 HSAT, capable of unlimited longitudinal testing. 1,184 tests were evaluated (Mean 7.9 tests/subject; SD 6.9). Subjects who reported using SA treatments were removed from analysis. Apnea/Hypopnea Index (AHI) was assessed to determine the presence and severity of SA. A subgroup of previously diagnosed individuals were assessed for accuracy of their original sleep study.

Results
On repeat testing, 69.7% of subjects had AHI scores that varied in severity. 51% had AHIs within the normal (&lt; 5) and abnormal range (&gt;5). The average AHI point difference between a subject's best and worst test was 12.4 (SD 12.1). Of the subjects that were positive for SA, 52% had false negatives on their first test. An average of 3.2 (SD 1.6) test were completed before obtaining a positive AHI, and 3.6 (SD 2.2) tests before arriving at the highest recorded severity. 26.1% of previously diagnosed subjects had HSAT-results that were more severe than their original diagnosis. Of the subjects that reported having mild SA, 11% had HSAT scores in the severe range. Of the subjects that reported having moderate SA, 38% HSAT scores in the severe range. Factors that were found to correlate with AHI variation were variation in time spent in supine position and time spent in REM.

Conclusion
Longitudinal testing with an HSAT confirmed high night-to-night variability of AHI in the majority of subjects. Subjects with mild to moderate SA were at high risk for misdiagnosis or misclassification if diagnosis is based on single or double night sleep studies. This study emphasized the need to standardize longitudinal testing and suggests that &gt;3 tests is ideal for diagnostic accuracy.

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0519 Predictors of False Negative HSATs in a Veteran Population
Authors: Hussain Shahnoor , Long Ariel , Mattison Carol , Stafford Erick , Ali Ibrahim Sultan 
Published: April 2024

Abstract

Introduction
Obstructive sleep apnea (OSA) is a common chronic condition associated with significant morbidity and mortality. Polysomnography (PSG) is the gold standard for diagnosis but is not always readily available. American Academy of Sleep Medicine (AASM) 2017 guidelines recommend in-lab PSG if a patient with high pretest probability of OSA has a negative home sleep apnea test (HSAT) defined as AHI &lt; 5 events/hour. At our institution, the threshold for obtaining an in-lab PSG has been broadened to include pAHI &lt; 10 events/hour. We investigated the validity of this threshold and analyzed co-morbidities and other predictors of false negative HSATs.

Methods
We reviewed 306 consecutive WatchPAT®️ (Itmar Medical, Ltd.) HSATs between January 2021 and December 2022 with a peripheral arterial tonometry–derived apnea-hypopnea index (pAHI) &lt; 10. False negative HSAT was defined as pAHI &lt; 10 with in-lab PSG AHI &gt; 5 and true negative HSAT was defined as pAHI &lt; 10 with in-lab PSG AHI &lt; 5. The in-lab PSG results were categorized as mild, moderate, or severe OSA. Demographic data and co-morbidity information were gathered for these patients. Chi-squared test was used for statistical analysis.

Results
Demographic data were well balanced between the two groups including male gender, age, and BMI. 131 patients met criteria for in-lab PSG. Of these, 64 (49%) were false negative and 67 (51%) were true negative. Out of the false negative HSATs, 65% were mild whereas 35% were moderate or severe OSA. These false negative HSATs were significantly associated with diabetes (p=0.01), hypertension (p=0.001), and patients with hypertension taking two or more anti-hypertensive medications (p=0.001).

Conclusion
False negative HSATs were common in the evaluation of OSA in our veteran population with a notable percentage of moderate to severe OSA on in-lab PSG even when the threshold was raised to pAHI &lt; 10. False negative HSATs were significantly associated with diabetes, hypertension, and patients on two or more anti-hypertensive medications. AASM guidelines recommend in-lab PSG for negative HSATs. Our study proposes that we should consider increasing our threshold to repeat a gold-standard in-lab PSG in patients with a pAHI &lt; 10, especially those with hypertension or diabetes.

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0520 Relationship Between ESS and AHI in PSG vs. HSAT: Possible Modulation by Sex
Authors: Lynch Mc Lyne , Dubrovsky Boris , Weingarten Jeremy , Cunningham John 
Published: April 2024

Abstract

Introduction
Excessive daytime sleepiness is a major symptom of OSA. However, the relationship between Epworth Sleepiness Scale (ESS) and apnea-hypopnea index (AHI) is tenuous and may depend on whether the AHI is derived from an in-lab or at-home study. We analyzed ESS as a function of demographic variables and AHI in a group of patients evaluated with type 3 home sleep apnea test (HSAT) or in-the lab PSG.

Methods
A group of 83 HSAT patients (40 women) were matched with 83 PSG patients by race and sex. Additionally, age was matched within a decade (20-29, 30-39, etc.), and BMI was matched within ±2.5 units. ESS was regressed onto age, BMI, sex, AHI, and AHI-by-sex, AHI-by-HSAT/PSG and AHI-by-sex-by-HSAT/PSG interactions in a backward regression model.

Results
The mean AHI=18.0±20.0, 119 patients had AHI≥5.0, OSA severity was equally represented on HSAT and PSG studies (chi-square=0.51, p=0.92). ESS was negatively related to AHI (B=-0.58, SE=0.25, p=0.022); however, all three interaction terms were significant: AHI-by-sex (B=0.30, SE=0.14, p=0.030), AHI-by-HSAT/PSG (B=0.37, SE=0.14, p=0.009) and AHI-by-sex-by-HSAT/PSG (B=0.20, SE=0.08, p=0.013). Analyzing men and women separately, women evidenced a negative relationship between ESS and AHI (B=-0.29, SE=0.13, p=0.023) and a significant AHI-by-HSAT/PSG interaction (B=0.18, SE=0.07, p=0.009). Among men, no explanatory variables or interaction terms were significantly related to ESS.

Conclusion
As suggested by this limited sample, the relationship between AHI and self-reported daytime sleepiness may depend on the type of test used to evaluate the respiratory status and may also be modulated by the patient’s sex. Future studies are required to elucidate the interaction between sex, AHI and the type of apnea test in the effects of these variables on subjective daytime sleepiness.

Support (if any)
none
0522 Screening and Diagnosis of Obstructive Sleep Apnea in Middle Eastern/North African (MENA) Women: A Pilot Study
Authors: Khalil Zeina Al , Attarian Hrayr , Sankari Abdulghani , Herreshoff Emily , Diemer Matthew , Duval Kim , Ismail Hadi , Sankari Safa , Sukhon Ahmad , Chervin Ron , Dunietz Galit Levi 
Published: April 2024

Abstract

Introduction
The risk of obstructive sleep apnea (OSA) increases with age for both sexes. Beyond chronological age, reproductive aging in women between ages of 40-60 may also raise OSA risk. Associations between reproductive aging and OSA have been infrequently explored among women from underrepresented ethnic groups. Therefore, we initiated a pilot cohort study in women from Middle Eastern/North African (MENA) backgrounds (n=77) from Southeast Michigan. To examine the frequency of OSA, we screened women with the STOP-BMI scoring scheme and then compared their risk profiles against Home Sleep Apnea Test (HSATs).

Methods
This pilot study included 77 MENA women from Michigan between the ages 40-60y. In addition to demographic, lifestyle and anthropometric data, women were also screened for OSA with the STOP-BMI questionnaire (Score &gt;3 indicating high risk and &lt; 3 indicating low risk). A subset of these women completed HSATs with WatchPAT®️ devices (pAHI 3% &gt;5/hour defined OSA).

Results
In this sample, the mean age was 48y and a third of women were postmenopausal. Mean BMI was 27.6 and 25% of the women had a BMI&gt;30. Twenty women underwent HSATs, and 15 (75%) showed OSA. The mean BMI of women with and without OSA was 30.6 and 26.8 kg/m2, respectively. Of the 15 women with OSA, eight scored low-risk on the STOP-BMI. Moreover, among women without OSA, 40% had a high-risk score on the STOP-BMI. Data suggested that the STOP-BMI has moderate sensitivity (47%) and specificity (60%).

Conclusion
We found a high frequency of OSA among midlife women from MENA ancestry and despite a low frequency of obesity. Moreover, the STOP-BMI screening tool for OSA was neither sensitive nor specific among these women. Our data suggest that OSA risk may be independent of BMI and that the current screening tool may not capture the symptoms and risk factors for OSA among midlife women.

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0526 Deep Convolutional Neural Network for Groaning and Snoring Sounds Classification
Authors: Yu Min , Gao Xuemei 
Published: April 2024

Abstract

Introduction
Catathrenia is a rare sleep-related breathing disorder characterized by recurrent monotonous groaning during sleep. The acoustic characteristics of groaning and snoring sounds have been investigated. This study aims to propose a deep convolutional neural network (CNN) for automatic binary classification.

Methods
This study consisted of 3728 episodes of groaning sounds and 4577 episodes of snoring sounds obtained from synchronized audio of full-night polysomnography. Four features extracted from log-scaled mel-spectrograms were used as input. The background gaussian noise and the time-shifting were used to augment the dataset. The dataset was randomly split into training (70%), validation (15%), and testing datasets (15%). A deep learning convolutional neural network architecture was trained and evaluated.

Results
The proposed CNN model achieves an accuracy of 95.0% on the binary classification of groaning and snoring sounds. The model attains a sensitivity/recall of 96.4%, a specificity of 93.4%, and an F1 score of 94.84%.

Conclusion
The proposed CNN architecture has performed well in the automatic binary classification of groaning and snoring sounds, which could reduce difficulties in acoustic analyses of groaning episode detection. The model needs to be verified with more audio data before it can be put into clinical use better.

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0527 Is Inpatient Polysomnography Associated with Limited Healthcare Access, and Economic and Social Deprivation?
Authors: Povitz Marcus , Nguyen Phuong Uyen , D'Souza Adam , Pendharkar Sachin , Kendzerska Tetyana 
Published: April 2024

Abstract

Introduction
In Alberta Canada care and funding models for sleep-disordered breathing (SDB) are fragmented, which may delay treatment particularly for socio-economically deprived individuals and contribute to the frequency and length of hospitalization. We assessed for economic and social deprivation as well as multimorbidity among adults undergoing diagnostic polysomnography (PSG) during hospitalization compared to age and sex-matched hospitalized and ambulatory controls.

Methods
In this retrospective health administrative data study, we included all individuals undergoing PSG during a hospitalization (SDB inpatients) from January 1, 2019, to December 31st, 2021, in a large, academic hospital-based sleep center in Calgary, Canada. We created time, age- and sex-matched control groups, each with a 2:1 ratio: 1) individuals referred to the sleep center (non-acute control); 2) hospitalized individuals who did not undergo PSG (acute control). Clinical records were linked to health administrative databases of routinely collected health variables, using validated algorithms to define comorbidities, social and economic deprivation, marginalization, and rurality. Between-group differences were assessed for statistical significance using paired analysis with a significance level of 0.05.

Results
We matched 275 individuals who underwent PSG during hospitalization with 550 acute and 550 non-acute controls. At baseline, SDB inpatients had greater health care utilization and a higher prevalence of comorbidities with a median [IQR] Charlson Comorbidity index of 3 [2, 5] vs 1 [0,3] for acute controls and 1 [0,2] for non-acute controls. Compared to the acute controls, SDB inpatients did not have higher rates of social deprivation or median marginalization (3 [2, 5] vs 3 [2,5]); however, they were more likely to be receiving social assistance (24% vs 12 %) and live in urban areas (88% vs 76%). Compared to non-acute control, SDB inpatients had a higher median rate of marginalization (3[2,5] vs 3[1,4]), lower median income (48925$ [38677,61632] vs 55252$ [44745,71310], and were more often receiving social assistance (24% vs. 14%).

Conclusion
SDB inpatients have a greater burden of multimorbidity and are more socially disadvantaged by several metrics. Follow-up studies are needed to determine if an earlier diagnosis could reduce the development of medical and social complications of untreated SDB.

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