Gold Standard Polysomnography Research Articles

Screening for obstructive sleep apnea in the memory clinic: A comparison of questionnaires, pulse oximetry, and polysomnography.

Obstructive sleep apnea (OSA) is highly prevalent among older adults and has been associated with cognitive decline and dementia risk. The suitability of screening tools for detecting OSA in memory clinic settings is unclear. To evaluate the utility and validity of the STOP-Bang questionnaire (SBQ) and pulse oximeter as a screening tool, compared to gold-standard polysomnography (PSG) in older adults attending a memory clinic. Participants aged over 50 with new onset cognitive/mood concerns attended a memory clinic, then completed the SBQ, oximetry, and PSG. The SBQ and oximetry's accuracy in detecting moderate-severe and severe OSA was evaluated using receiver operating curves. Intraclass correlation and Bland-Altman plots compared the oximeter's adjusted oxygen desaturation index (ODI-Ox) and PSG's apnea-hypopnea index (AHI-PSG). Of 194 participants (mean age = 65.6, 64 males) who completed PSG, 184 completed the SBQ, and 138 completed oximetry. SBQ demonstrated limited performance for moderate-severe OSA (sensitivity = 52%, specificity = 62%, AUC = 0.600) and severe OSA (sensitivity = 18%, specificity = 87%, AUC = 0.577). Oximetry was satisfactory for moderate-severe OSA (sensitivity = 67%, specificity = 73%, AUC = 0.769) and severe OSA (sensitivity = 50%, specificity = 88%, AUC = 0.730). The diagnostic performance was improved with new cut-offs at ODI-Ox ≥ 11 for AHI-PSG ≥ 15 and ODI-Ox ≥ 20 for AHI-PSG ≥ 30. Bland-Altman plots and intraclass correlation indicated acceptable agreement for oximetry. The findings suggest that while the SBQ may be unsuitable to detect moderate or severe OSA for older adults with cognitive impairment, oximetry may be a viable screening tool. Given OSA treatment can optimize sleep and may slow cognitive decline, routine screening for OSA should be part of memory clinic assessments.

A Novel Screening Tool for Severe Obstructive Sleep Apnea.

Purpose: Obstructive sleep apnea (OSA) is a prevalent condition with long waiting lists for the gold standard polysomnography (PSG) test. Screening tests are essential to identify and prioritize patients with severe OSA. Current questionnaires do not accurately detect severe cases. This study aims to develop an alternative screening tool based on anthropometric and morphological characteristics to determine the severity of OSA. Methods: The study included 149 patients with sleep apnea symptoms who did not have additional diseases. The obstructive sleep apnea morphologic scale (OSAMS) was created using 5 parameters: 3 internal parameters related to the upper respiratory tract (tonsil size, oropharyngeal passage, and hypopharyngeal-laryngeal examination) and 2 external parameters (neck circumference and body mass index). Each parameter was scored, and the total scores were correlated with the apnea-hypopnea index (AHI) of PSG to determine the severity of OSA. Results: A statistically significant moderate positive correlation was found between AHI and OSAMS scores (rho = .491; P < .001). A grand total score >4 provided 80.3% sensitivity and 53.9% specificity to identify patients with severe OSA. OSAMS demonstrated superior results compared to existing questionnaires for OSA screening in our study group. Conclusions: OSAMS is a potential alternative to existing questionnaires to screen patients with severe OSA, offering a practical approach based on morphology during physical examinations.

Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI.

Understanding the function of sleep requires studying the dynamics of brain activity across whole-night sleep and their transitions. However, current gold standard polysomnography (PSG) has limited spatial resolution to track brain activity. Additionally, previous fMRI studies were too short to capture full sleep stages and their cycling. To study whole-brain dynamics and transitions across whole-night sleep, we used an unsupervised learning approach, the Hidden Markov model (HMM), on two-night, 16 hr fMRI recordings of 12 non-sleep-deprived participants who reached all PSG-based sleep stages. This method identified 21 recurring brain states and their transition probabilities, beyond PSG-defined sleep stages. The HMM trained on one night accurately predicted the other, demonstrating unprecedented reproducibility. We also found functionally relevant subdivisions within rapid eye movement (REM) and within non-REM 2 stages. This study provides new insights into brain dynamics and transitions during sleep, aiding our understanding of sleep disorders that impact sleep transitions.

Validation of a Textile-Based Wearable Measuring Electrocardiogram and Breathing Frequency for Sleep Apnea Monitoring

Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up.

Portable polygraphic device (Somnocheck micro CARDIO®) provides accurate diagnostic information in psychiatric patients at risk for obstructive sleep apnoea: an observational cohort study

BackgroundSymptoms of obstructive sleep apnoea (OSA) overlap significantly with those of psychiatric disorders, making accurate diagnosis of OSA challenging within psychiatric settings. Diagnosing OSA in psychiatric patients is crucial because untreated OSA can exacerbate psychiatric symptoms, reduce treatment efficacy, and impair overall quality of life. This study aimed to determine the diagnostic accuracy of a readily accessible procedure for psychiatric patients in a real-world clinical setting by comparing the Somnocheck micro CARDIO® (SCm) portable cardiorespiratory polygraphy device with the gold standard polysomnography (PSG).MethodsThis observational cohort study included consecutive psychiatric patients at intermediate to high risk for OSA based on screening with the STOP-Bang questionnaire, admitted to a single tertiary care centre between June 1, 2016 and December 31, 2022. The Apnoea-Hypopnoea-Index (AHI), Apnoea-Index (AI), Oxygen-Desaturation-Index (ODI), and minimum oxygen saturation were measured sequentially by SCm and PSG.ResultsA total of 57 patients were analysed (median age 62.0 [Interquartile Range (IQR), 51.5–72.5] years; 34 [59.6%] men). Regarding AHI, no significant differences (AHI measured by PSG, median, 16.6 [IQR, 6.2–26.7] vs. AHI measured by SCm, median, 14.9 [IQR, 10.0-22.8]; p = 0.812; r = 0.71) were found between SCm and PSG. AI, ODI and minimum oxygen saturation differed significantly between SCm and PSG. Using optimised cut-off values (any OSA: AHISCm ≥ 9.25), SCm showed high sensitivity (0.894) and high specificity (0.800) for the diagnosis of OSA, with an area under the receiver operating characteristic curve of 0.877.ConclusionsThis study found that the SCm portable device was accurate in identifying psychiatric patients with OSA. AHI measurement by SCm provided reliable diagnostic performance in comparison with the gold standard polysomnography. These findings support the integration of polygraphic measurements into the routine sleep assessment of psychiatric patients. Early and accurate diagnosis of OSA in this population can significantly improve the management of both sleep disorders and psychiatric conditions, potentially enhancing overall treatment outcomes and quality of life for these patients.

Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI.

Understanding the function of sleep requires studying the dynamics of brain activity across whole-night sleep and their transitions. However, current gold standard polysomnography (PSG) has limited spatial resolution to track brain activity. Additionally, previous fMRI studies were too short to capture full sleep stages and their cycling. To study whole-brain dynamics and transitions across whole-night sleep, we used an unsupervised learning approach, the Hidden Markov model (HMM), on two-night, 16-hour fMRI recordings of 12 non-sleep-deprived participants who reached all PSG-based sleep stages. This method identified 21 recurring brain states and their transition probabilities, beyond PSG-defined sleep stages. The HMM trained on one night accurately predicted the other, demonstrating unprecedented reproducibility. We also found functionally relevant subdivisions within rapid eye movement (REM) and within non-REM 2 stages. This study provides new insights into brain dynamics and transitions during sleep, aiding our understanding of sleep disorders that impact sleep transitions.

Automated remote sleep monitoring needs uncertainty quantification.

Wearable electroencephalography devices emerge as a cost-effective and ergonomic alternative to gold-standard polysomnography, paving the way for better health monitoring and sleep disorder screening. Machine learning allows to automate sleep stage classification, but trust and reliability issues have hampered its adoption in clinical applications. Estimating uncertainty is a crucial factor in enhancing reliability by identifying regions of heightened and diminished confidence. In this study, we used an uncertainty-centred machine learning pipeline, U-PASS, to automate sleep staging in a challenging real-world dataset of single-channel electroencephalography and accelerometry collected with a wearable device from an elderly population. We were able to effectively limit the uncertainty of our machine learning model and to reliably inform clinical experts of which predictions were uncertain to improve the machine learning model's reliability. This increased the five-stage sleep-scoring accuracy of a state-of-the-art machine learning model from 63.9% to 71.2% on our dataset. Remarkably, the machine learning approach outperformed the human expert in interpreting these wearable data. Manual review by sleep specialists, without specific training for sleep staging on wearable electroencephalography, proved ineffective. The clinical utility of this automated remote monitoring system was also demonstrated, establishing a strong correlation between the predicted sleep parameters and the reference polysomnography parameters, and reproducing known correlations with the apnea-hypopnea index. In essence, this work presents a promising avenue to revolutionize remote patient care through the power of machine learning by the use of an automated data-processing pipeline enhanced with uncertainty estimation.

Leveraging 3D convolutional neural network and 3D visible-near-infrared multimodal imaging for enhanced contactless oximetry.

Monitoring oxygen saturation ( ) is important in healthcare, especially for diagnosing and managing pulmonary diseases. Non-contact approaches broaden the potential applications of measurement by better hygiene, comfort, and capability for long-term monitoring. However, existing studies often encounter challenges such as lower signal-to-noise ratios and stringent environmental conditions. We aim to develop and validate a contactless measurement approach using 3D convolutional neural networks (3D CNN) and 3D visible-near-infrared (VIS-NIR) multimodal imaging, to offer a convenient, accurate, and robust alternative for monitoring. We propose an approach that utilizes a 3D VIS-NIR multimodal camera system to capture facial videos, in which is estimated through 3D CNN by simultaneously extracting spatial and temporal features. Our approach includes registration of multimodal images, tracking of the 3D region of interest, spatial and temporal preprocessing, and 3D CNN-based feature extraction and regression. In a breath-holding experiment involving 23 healthy participants, we obtained multimodal video data with reference values ranging from 80% to 99% measured by pulse oximeter on the fingertip. The approach achieved a mean absolute error (MAE) of 2.31% and a Pearson correlation coefficient of 0.64 in the experiment, demonstrating good agreement with traditional pulse oximetry. The discrepancy of estimated values was within 3% of the reference for of all 1-s time points. Besides, in clinical trials involving patients with sleep apnea syndrome, our approach demonstrated robust performance, with an MAE of less than 2% in estimations compared to gold-standard polysomnography. The proposed approach offers a promising alternative for non-contact oxygen saturation measurement with good sensitivity to desaturation, showing potential for applications in clinical settings.

Association Between Sleep Quality and Cognitive Symptoms in Patients with Major Depressive Disorder.

Cognitive symptoms and sleep disturbance (SD) are common non-mood-related symptoms of major depressive disorder (MDD). In clinical practice, both cognitive symptoms and SD are related to MDD progression. However, there are only a few studies investigating the connection between cognitive symptoms and SD in patients with MDD, and only preliminary evidence suggests a significant association between cognitive symptoms and SD in patients with mood disorders. This study investigates the relationship between cognitive symptoms and sleep quality in patients with major depressive disorder. Patients (n = 20) with MDD were enrolled; their mean Hamilton Depression Scale-17 score was 21.95 (±2.76). Gold standard polysomnography (PSG) was used to assess sleep quality, and the validated THINC-integrated tool (the cognitive screening tool) was used to evaluate cognitive function in MDD patients. Overall, the results showed significant correlations between the cognitive screening tool's total score and sleep latency, wake-after-sleep onset, and sleep efficiency. These findings indicate that cognitive symptoms are associated with poor sleep quality among patients with MDD.

Sleep Disturbance During Post-Traumatic Amnesia and Early Recovery After Traumatic Brain Injury.

After moderate to severe traumatic brain injury (TBI), sleep disturbance commonly emerges during the confused post-traumatic amnesia (PTA) recovery stage. However, the evaluation of early sleep disturbance during PTA, its recovery trajectory, and influencing factors is limited. This study aimed to evaluate sleep outcomes in patients experiencing PTA using ambulatory gold-standard polysomnography (PSG) overnight and salivary endogenous melatonin (a hormone that influences the sleep-wake cycle) assessment at two time-points. The relationships between PSG-derived sleep-wake parameters and PTA symptoms (i.e., agitation and cognitive disturbance) were also evaluated. In a patient subset, PSG was repeated after PTA had resolved to assess the trajectory of sleep disturbance. Participants with PTA were recruited from Epworth HealthCare's inpatient TBI Rehabilitation Unit. Trained nurses administered overnight PSG at the patient bedside using the Compumedics Somté portable PSG device (Compumedics, Ltd., Australia). Two weeks after PTA had resolved, PSG was repeated. On a separate evening, two saliva specimens were collected (at 24:00 and 06:00) for melatonin testing. Results of routine daily hospital measures (i.e., Agitated Behavior Scale and Westmead PTA Scale) were also collected. Twenty-nine patients were monitored with PSG (mean: 41.6 days post-TBI; standard deviation [SD]: 28.3). Patients' mean sleep duration was reduced (5.6 h, SD: 1.2), and was fragmented with frequent awakenings (mean: 27.7, SD: 15.0). Deep, slow-wave restorative sleep was reduced, or completely absent (37.9% of patients). The use of PSG did not appear to exacerbate patient agitation or cognitive disturbance. Mean melatonin levels at both time-points were commonly outside of normal reference ranges. After PTA resolved, patients (n = 11) displayed significantly longer mean sleep time (5.3 h [PTA]; 6.5 h [out of PTA], difference between means: 1.2, p = 0.005). However, disturbances to other sleep-wake parameters (e.g., increased awakenings, wake time, and sleep latency) persisted after PTA resolved. This is the first study to evaluate sleep disturbance in a cohort of patients as they progressed through the early TBI recovery phases. There is a clear need for tailored assessment of sleep disturbance during PTA, which currently does not form part of routine hospital assessment, to suggest new treatment paradigms, enhance patient recovery, and reduce its long-term impacts.

A sleep staging model based on core body temperature rhythm

Core body temperature (CBT) rhythm and sleep process are closely intertwined, yet there is a lack of quantitative methods to link them. To accomplish the research link of “sleeping thermal environment - body temperature - sleep quality” and also to establish a new sleep staging methodology, sleep staging models based on random forest algorithm were constructed. These models fulfilled the requirement of distinguishing four sleep stages (Waking stage, REM stage, Light sleep and Deep sleep) based on the CBT rhythm. The database utilized for model construction emanated from a winter sleep experiment, wherein seven male and seven female participants underwent a consecutive three-night testing protocol within laboratory settings. Data analysis results confirmed that (1) For the separately constructed sleep staging models for males and females, their accuracies stood at 0.70 and 0.77, respectively. The overall performance of the female model marginally surpassed that of the male model; (2) The predominant error in existing models arisen from misjudgments of adjacent sleep stages, such as misclassifying REM stage and deep sleep as light sleep, and this phenomenon may arise from the individual difference in CBT distribution; (3) Personalized sleep staging models have promising prospects, with their peak accuracy reaching an impressive 95% - nearly approaching the performance of gold standard polysomnography (PSG). In summary, this study elucidates the inherent quantitative connection between CBT rhythm and sleep stages, and the proposed sleep staging model has practical value.

Performance of cardiorespiratory-based sleep staging in patients using beta blockers.

Automatic sleep staging based on cardiorespiratory signals from home sleep monitoring devices holds great clinical potential. Using state-of-the-art machine learning, promising performance has been reached in patients with sleep disorders. However, it is unknown whether performance would hold in individuals with potentially altered autonomic physiology, for example under the influence of medication. Here, we assess an existing sleep staging algorithm in patients with sleep disorders with and without the use of beta blockers. We analyzed a retrospective dataset of sleep recordings of 57 patients with sleep disorders using beta blockers and 57 age-matched patients with sleep disorders not using beta blockers. Sleep stages were automatically scored based on electrocardiography and respiratory effort from a thoracic belt, using a previously developed machine-learning algorithm (CReSS algorithm). For both patient groups, sleep stages classified by the model were compared to gold standard manual polysomnography scoring using epoch-by-epoch agreement. Additionally, for both groups, overall sleep parameters were calculated and compared between the two scoring methods. Substantial agreement was achieved for four-class sleep staging in both patient groups (beta blockers: kappa = 0.635, accuracy = 78.1%; controls: kappa = 0.660, accuracy = 78.8%). No statistical difference in epoch-by-epoch agreement was found between the two groups. Additionally, the groups did not differ on agreement of derived sleep parameters. We showed that the performance of the CReSS algorithm is not deteriorated in patients using beta blockers. Results do not indicate a fundamental limitation in leveraging autonomic characteristics to obtain a surrogate measure of sleep in this clinically relevant population. Hermans L, van Meulen F, Anderer P, etal. Performance of cardiorespiratory-based sleep staging in patients using beta blockers. J Clin Sleep Med. 2024;20(4):575-581.

Prevalence of Obstructive Sleep Apnea Using Home Sleep Test in Taiwan During the Coronavirus Disease Pandemic.

Obstructive sleep apnea syndrome (OSAS) is a common disorder associated with serious sequelae. The current gold standard diagnostic method, polysomnography, is costly and time consuming and requires patients to stay overnight at a facility. This study aimed to reveal the prevalence of OSAS in general adult population using a home sleep test (HST) during the coronavirus disease 2019 (COVID-19) pandemic. This prospective cohort study was conducted by the Department of Otolaryngology, Taipei City Hospital, Taipei, Taiwan, between January 2020 and December 2021. A total of 1372 patients aged 30-70 years completed an HST using a Type 3 portable sleep monitor (PM). The apnea-hypopnea index (AHI) was analyzed to assess the association of OSAS with age, body mass index (BMI), sex, Epworth Sleepiness Scale (ESS) and the Sleep Apnea Risk Assessment questionnaire (STOP-Bang questionnaire) rating. The mean age of the patients (782 men, 57%; 590 women, 43%) was 49.24 ± 11.04 years. OSAS was detected in 954 (69.5%) patients with 399 (29.1%) mild OSAS; 246 (17.9%) moderate OSAS; and 309 (22.5%) severe OSAS. Among these, the prevalence of moderate-to-severe OSAS was 143 (10.4%) in women and 412 (30.0%) in men. The mean age was the highest (51.29 ± 11.29) in the mild OSAS group and lowest (47.08 ± 10.87) in the healthy group. OSAS severity was greater with increasing BMI, 23.39 ± 3.44 in the healthy group and 29.29 ± 5.01 in the severe OSAS group. A positive correlation was also noted between the ESS/STOP-Bang questionnaire rating and OSAS severity. The prevalence of OSAS in Taiwan was 69.5% in our study. It showed strong evidence that OSAS has important public health consequences and PMs are simple, fast, feasible, and cost-effective tools for OSAS screening in the home environment, especially during the COVID-19 pandemic.

Can we rely on wearable sleep-tracker devices for fatigue management?

Wearable sleep-tracker devices are ubiquitously used to measure sleep; however, the estimated sleep parameters often differ from the gold-standard polysomnography (PSG). It is unclear to what extent we can tolerate these errors within the context of a particular clinical or operational application. Here, we sought to develop a method to quantitatively determine whether a sleep tracker yields acceptable sleep-parameter estimates for assessing alertness impairment. Using literature data, we characterized sleep-measurement errors of 18 unique sleep-tracker devices with respect to PSG. Then, using predictions based on the unified model of performance, we compared the temporal variation of alertness in terms of the psychomotor vigilance test mean response time for simulations with and without added PSG-device sleep-measurement errors, for nominal schedules of 5, 8, or 9 hours of sleep/night or an irregular sleep schedule each night for 30 consecutive days. Finally, we deemed a device error acceptable when the predicted differences were smaller than the within-subject variability of 30 milliseconds. We also established the capability to estimate the extent to which a specific sleep-tracker device meets this acceptance criterion. On average, the 18 sleep-tracker devices overestimated sleep duration by 19 (standard deviation = 44) minutes. Using these errors for 30 consecutive days, we found that, regardless of sleep schedule, in nearly 80% of the time the resulting predicted alertness differences were smaller than 30 milliseconds. We provide a method to quantitatively determine whether a sleep-tracker device produces sleep measurements that are operationally acceptable for fatigue management.

Somno-Art Software identifies pathology-induced changes in sleep parameters similarly to polysomnography.

Polysomnographic sleep architecture parameters are commonly used to diagnose or evaluate treatment of sleep disorders. Polysomnography (PSG) having practical constraints, the development of wearable devices and algorithms to monitor and stage sleep is rising. Beside pure validation studies, it is necessary for a clinician to ensure that the conclusions drawn with a new generation wearable sleep scoring device are consistent to the ones of gold standard PSG, leading to similar interpretation and diagnosis. This paper reports on the performance of Somno-Art Software for the detection of differences in sleep parameters between patients suffering from obstructive sleep apnea (OSA), insomniac or major depressive disorder (MDD) compared to healthy subjects. On 244 subjects (n = 26 healthy, n = 28 OSA, n = 66 insomniacs, n = 124 MDD), sleep staging was obtained from PSG and Somno-Art analysis on synchronized electrocardiogram and actimetry signals. Mixed model analysis of variance was performed for each sleep parameter. Possible differences in sleep parameters were further assessed with Mann-Whitney U-test between the healthy subjects and each pathology group. All sleep parameters, except N1+N2, showed significant differences between the healthy and the pathology group. No significant differences were observed between Somno-Art Software and PSG, except a 3.6±2.2 min overestimation of REM sleep. No significant interaction 'group'*'technology' was observed, suggesting that the differences in pathologies are independent of the technology used. Overall, comparable differences between healthy subjects and pathology groups were observed when using Somno-Art Software or polysomnography. Somno-Art proposes an interesting valid tool as an aid for diagnosis and treatment follow-up in ambulatory settings.

Relationship between respiratory event-related arousal and increased pulse rate in patients with obstructive sleep apnea

Objective: To investigate the relationship between respiratory event-related arousal and increased pulse rate in patients with obstructive sleep apnea (OSA), and to evaluate whether elevated pulse rate can be used as a surrogate marker of arousal. Methods: A total of 80 patients [40 males and 40 females, age range (18-63 years), mean age (37±13) years] who attended the Sleep Center of the Department of Respiratory and Critical Care Medicine, Tianjin Medical University General Hospital for polysomnography (PSG) from January 2021 to August 2022 were enrolled. Stable PSG recordings of non-rapid eye movement (NREM) sleep to compare the mean pulse rate (PR), the lowest PR 10 seconds before the onset of arousal, and the highest PR within 10 seconds after the end of arousal associated with each respiratory event. At the same time, the correlation between the arousal index and the pulse rate increase index (PRRI), as well as ΔPR1 (highest PR-lowest PR) and ΔPR2 (highest PR-mean PR), respectively, with the duration of respiratory events, the duration of arousal, the magnitude of pulse oximetry (SpO2) decline, and the lowest SpO2 was analyzed. Among the 53 patients, 10 events without arousal and 10 events with arousal (matched for the magnitude of SpO2 decline) were selected for NREM in each of the 53 patients, and ΔPR before and after termination of respiratory events in the two groups was compared. In addition, 50 patients were simultaneously subjected to portable sleep monitoring (PM) and divided into non-severe OSA group (n=22) and severe OSA group (n=28), and ΔPR≥3 times,≥6 times,≥9 times, and≥12 times after respiratory events were used as surrogate markers of arousal, and ΔPR was scored manually and integrated into the respiratory event index (REI) of PM. Then, we compared the agreement between REI calculated from the four PR cut-off points and the apnea-hypopnea index (AHIPSG) calculated by the gold standard PSG. Results: ΔPR1 [(13±7)times/min] and ΔPR2 [(11±6)times/min] were significantly higher in patients with severe OSA than in patients with non-OSA,mild and moderate OSA. The arousal index was positively correlated with the four PRRIs (r 0.968, 0.886, 0.773, 0.687, P<0.001, respectively), and the highest PR [(77±12) times/min] within 10 s after the end of arousal was significantly higher than the lowest PR [(65±10) times/min, t=113.24, P<0.001] and the mean PR [(67±11) times/min, t=103.02, P<0.001]. ΔPR1 and ΔPR2 were moderately correlated with the decrease in SpO2 (r=0.490, 0.469, P<0.001). After matching the magnitude of SpO2 decline, the ΔPR[(9±6)/min] before and after the termination of respiratory events with arousal was significantly higher than that of respiratory events without arousal [(6±5)/min, t=7.72, P<0.001]. The differences between REI+PRRI3 and REI+PRRI6 and AHIPSG in the non-severe OSA group were not statistically significant (P values 0.055 and 0.442, respectively), and REI+PRRI6 and AHIPSG showed good agreement (the mean difference was 0.7 times/h, 95%CI 8.3-7.0 times/h). The four indicators of PM in the severe OSA group were statistically different from AHIPSG (all P<0.05), and the agreement was poor. Conclusions: Respiratory event-related arousal in OSA patients is independently associated with increased PR, and frequent arousal may lead to increased frequency of PR fluctuations, and elevated PR may be used as a surrogate marker of arousal, especially in patients with non-severe OSA, where elevated PR≥6 times significantly improves the diagnostic agreement between PM and PSG.

Revisiting the Epworth sleepiness scale : Is excessive daytime sleepiness still avalid screening tool for obstructive sleep apnea in apopulation at risk?

Obstructive sleep apnea (OSA) is acommon medical problem with numerous comorbidities and high costs. Since the introduction of the Epworth sleepiness scale (ESS), excessive daytime sleepiness (EDS) has been considered the most common and prominent symptom of OSA. Aim of this study was to re-evaluate the ESS for detection of OSA in apopulation at risk compared to the gold standard overnight polysomnography (PSG). A total of 266patients (mean age 57.9 ± 11.6 years; 189 men and 77women), referred to our sleep laboratory for probable OSA, were given ESS followed by an overnight PSG. The ESS values were compared to PSG apnea hypopnea index (AHI) with sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy (DA) calculated for ESS. The positive cut-off value for ESS was ≥ 10 and for AHI ≥ 5. Only 92(34.6%) subjects had a positive ESS. An OSA was diagnosed by PSG in 213 (80.1%) subjects: 46 having mild, 37 moderate and 130 severe apnea. Most subjects with positive ESS (88.0%) were found to have OSA but most subjects with a negative ESS (75.9%) were also positive for OSA (42% with AHI ≥ 30). The area under the receiver operating characteristic (ROC) curve for ESS was 0.60 (95% confidence interval, CI 0.54-0.66; p = 0.020) with SE 38.0%, SP 79.3%, PPV 88.0%, NPV 24.1% and DA 46.2%. It was found that excessive daytime sleepiness, measured by ESS, is not avaluable screening tool for OSA, especially when the test is negative. Other screening tests that involve additional parameters, beside daytime sleepiness alone, should be considered.

Objective assessment of sleep parameters using multimodal AX3 data in older participants

AbstractBackgroundSleep disturbances are both risk factors for and symptoms of dementia. Current methods for assessing sleep disturbances are largely based on either polysomnography (PSG) which is costly and inconvenient, or self‐ or care‐giver reports which are prone to measurement error. Low‐cost methods to monitor sleep disturbances longitudinally and at scale can be useful for assessing symptom development. Here, we develop deep learning models that use multimodal variables (accelerometers and temperature) recorded by the AX3 to accurately identify sleep and wake epochs and derive sleep parameters.MethodEighteen men and women (65‐80y) participated in a sleep laboratory‐based study in which multiple devices for sleep monitoring were evaluated. PSGs were recorded over a 10‐h period and scored according to established criteria per 30 sec epochs. Tri‐axial accelerometers and temperature signals were captured with an Axivity AX3, at 100Hz and 1Hz, respectively, throughout a 19‐h period, including 10‐h concurrent PSG recording and 9‐h of wakefulness. We developed and evaluated a supervised deep learning algorithm to detect sleep and wake epochs and determine sleep parameters from the multimodal AX3 raw data. We validated our results with gold standard PSG measurements and compared our algorithm to the Biobank accelerometer analysis toolbox. Single modality (accelerometer or temperature) and multimodality (both signals) approaches were evaluated using the 3‐fold cross‐validation.ResultThe proposed deep learning model outperformed baseline models such as the Biobank accelerometer analysis toolbox and conventional machine learning classifiers (Random Forest and Support Vector Machine) by up to 25%. Using multimodal data improved sleep and wake classification performance (up to 18% higher) compared with the single modality. In terms of the sleep parameters, our approach boosted the accuracy of estimations by 11% on average compared to the Biobank accelerometer analysis toolbox.ConclusionIn older adults without dementia, combining multimodal data from AX3 with deep learning methods allows satisfactory quantification of sleep and wakefulness. This approach holds promise for monitoring sleep behaviour and deriving accurate sleep parameters objectively and longitudinally from a low‐cost wearable sensor. A limitation of our current study is that the participants were healthy older adults: future work will focus on people living with dementia.

0194 Sleep Tracker: How Accurate Does It Need To Be?

Abstract Introduction Commercial sleep tracker devices offer the ability to measure an individual’s sleep patterns, providing a portable and low-cost option to collect sleep-wake data to enhance fatigue management and minimize alertness impairment. While multiple studies have reported that sleep parameters estimated with these devices differ from gold-standard polysomnography (PSG), the extent to which these differences are acceptable is not known. Here, we provide a method to quantitatively assess whether sleep-duration errors generated by sleep trackers hinder our ability to assess alertness impairment. Methods To obtain representative statistics of sleep-duration errors, we performed a literature review and identified 14 studies that compared estimations of sleep parameters between 18 unique commercial sleep trackers and PSG. Then, we used the well-validated Unified Model of Performance (UMP), a mathematical model that predicts alertness impairment as a function of sleep history, to quantitatively assess the effect of sleep-duration errors on alertness predictions. For this purpose, we performed 20,000 simulations in which an individual slept 8 hour per night for 30 consecutive days. Each day, we randomly sampled sleep-duration errors using the obtained representative statistics and computed daily alertness-prediction errors. We repeated these simulations for 5 and 9 hours of sleep per night. We assumed that sleep-duration errors that lead to alertness-prediction errors of ≤30 ms (i.e., the within-subject variability under well-rested conditions) are not a concern. Results Based on the 14 studies, sleep trackers yielded sleep-duration errors with a mean of 22 minutes and a standard deviation of 44 minutes. Using these statistics to sample daily errors, we found that, at the end of the 30 days of UMP simulations, these sleep-duration errors led to a mean alertness-prediction error of ~20 ms, with ~75% of the errors ≤30 ms. We also found that, for each additional minute of sleep-duration error, the prediction error increased by ~1 ms. These results were consistent when the hours of sleep per night varied from 5 to 9 hours. Conclusion On average, sleep trackers lead to alertness-prediction errors that are consistently smaller than the within-subject variability of alertness impairment, suggesting that they are an alternative approach for measuring sleep duration. Support (if any)

0433 Home sleep apnea testing in patients with a LVAD: Is it feasible?

Abstract Introduction Sleep related breathing disorders, including central sleep apnea (CSA), are frequent in patients with heart failure however, only limited data is known about the effect of left ventricular assist device (LVAD) placement on OSA and even less about the accuracy of home sleep apnea testing in this population (HSAT). Performing the gold standard polysomnography (PSG) is challenging in this cohort due to logistics, especially during the COVID-19 pandemic. Extending HSAT to the LVAD population would be beneficial, yet initial tests with the WatchPat® were deemed inconclusive. We decided to evaluate the validity of NOX-T3 device for HSAT compared to PSG. Methods This was a single center prospective, observational study that included LVAD patients who screened positive on the STOP-BANG questionnaire. A sleep study was indicated based on consultation with a sleep physician and a standard PSG was ordered. During the PSG, the NOX-T3 was also applied with the results analyzed and compared after test completion. Results In this preliminary analysis, 4 patients (3 male, 1 female) with a LVAD completed a PSG along with the NOX-T3. The overall severity of sleep apnea indicated by apnea hypopnea index during the PSG and the respiratory event index during the NOX-T3 were similar in this small cohort. In 2 of the 4 patients, the oximetry finding (minutes with oxygen saturations at or below 88%) were also similar during both the PSG and the NOX-T3. Conclusion Based on this preliminary analysis, data obtained with the NOX-T3 system were valid and compared well to the standard of care PSG in patients with a LVAD. We plan to enroll at least 10 patients in this ongoing trial, further studies will be needed to validate these results. Performing HSAT with the NOX-T3 in this unique population if PSG is not feasible would be of significant value given the morbidity of sleep apnea in this population. Support (if any)