Gold Standard Polysomnography Research Articles

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)

0187 Hypnograms for 365 Nights Predicts Subjective Sleep Quality in Healthy Adults: Results from the Ultra Long-Term Sleep (ULTS) Study

Abstract Introduction Sleep quality is fundamental to our somatic and mental health. However, the relationship between subjective sleep quality and sleep architecture remains poorly understood. New wearable or minimally invasive technologies facilitate the recording of electroencephalography (EEG) with lower spatial resolution than standard EEG but much greater longitudinal dispersion. This enables investigation of day-to-day variation in sleep measured directly with EEG. This study will compare EEG-derived sleep parameters with covariates such as sustained attention and subjective sleep quality. Methods Twenty-five healthy adults were implanted with a two-channel subcutaneous EEG (sqEEG) lead. Twenty subjects completed the 1-year protocol (average 32±13 years of age). Their sqEEG signals were recorded each night for 1 year alongside a morning 3-minute Psychomotor Vigilance Task (PVT) and self-reported sleep quality, which included Karolinska Sleepiness Scale (KSS). A deep learning model, U-Sleep, was fine-tuned on sqEEG with synchronized gold standard polysomnography used as ground truth. Hypnograms and sleep parameters were thus automatically calculated. Results Subjective sleep quality measured by KSS revealed a moderate negative correlation with rapid-eye-movement (REM) duration (r=-0.31, 95% CI=(-0.31, -0.31)), and total sleep time (TST) (r=-0.31, 95% CI=(-0.31, -0.31)). There was a moderate correlation between KSS and mean PVT reaction time (r=0.21, 95% CI=(0.21, 0.22)). There was a low negative correlation between PVT and TST (r=-0.1). Preliminary results indicate a moderate correlation between sleep parameters and subjective sleep quality. The correlations with PVT were lower, which suggests that 3-minute PVT is not sensitive to TST in normal sleep. However, the correlation between PVT and KSS suggests that PVT does predict subjective sleep quality, but to a smaller degree than standard sleep parameters. Conclusion Measuring day-to-day variation in high-quality EEG-based sleep recordings has the potential of creating a new branch in sleep medicine. Patients can be evaluated not only by findings in a single recording but the stability and variation of all findings can be analyzed. Preliminary results suggest that subjective sleep quality can be predicted directly from sqEEG and potentially be explained by behavioral factors in a subsequent cause-effect analysis. Support (if any) The project is supported by Innovation Fund Denmark, UNEEG medical, and T&amp;W Engineering.

Evaluation of a novel device to assess obstructive sleep apnea and body position.

Obstructive sleep apnea is a prevalent disease with well-known complications when left untreated. Advances in sleep-disordered breathing diagnosis may increase detection and appropriate treatment. The Wesper device is a recently developed portable system with specialized wearable patches that can measure respiratory effort, derived airflow, estimated air pressure, and body position. This study sought to compare the diagnostic ability of the novel Wesper device with the gold standard of polysomnography. Patients enrolled in the study underwent simultaneous polysomnography and Wesper device testing in a sleep laboratory setting. Data were collected and scored by readers blinded to all patient information, and the primary reader was blinded to testing method. The accuracy of the Wesper device was determined by calculation of the Pearson correlation and Bland-Altman limits of agreement of apnea-hypopnea indices between testing methods. Adverse events were also recorded. A total of 53 patients were enrolled in the study and 45 patients were included in the final analysis. Pearson correlation between polysomnography and Wesper device apnea-hypopnea index determinations was 0.951, which met the primary endpoint goal (P = .0003). The Bland-Altman 95% limits of agreement were -8.05 and 6.38, which also met the endpoint goal (P < .001). There were no adverse events or serious adverse events noted. The Wesper device compares favorably with gold-standard polysomnography. Given the lack of safety concerns, we advocate for further study regarding its utility in diagnosis and management of sleep apnea in the future. Raphelson JR, Ahmed IM, Ancoli-Israel S, etal. Evaluation of a novel device to assess obstructive sleep apnea and body position. J Clin Sleep Med. 2023;19(9):1643-1649.

Assessment of obstructive sleep apnea severity using audio-based snoring features

Background and ObjectiveSnoring is a prima symptom of obstructive sleep apnea (OSA). Here, we add audio-based snoring features to improve the non-obtrusive assessment of sleep apnea, by estimating the apnea-hypopnea index (AHI) and classifying OSA severity. MethodsWe propose novel features to quantify temporal changes between snores (snore rate variability) and to describe trends in snore energy, based on the assessment of snore sounds from audio signals over the full night. We then combined those features with age, body mass index (BMI) and features described in literature. An extreme gradient boosting algorithm was trained with all these features on AHI estimation. The estimated AHI was then used to classify OSA severity. ResultsAudio-based estimated AHI showed a significant Spearman's correlation with the AHI based on gold-standard polysomnography (R = 0.786, P < 0.0001). Our results outperformed a model trained with solely previously described features in our dataset (R = 0.676, P < 0.0001) and a model trained with the combination of previously described features, age, and BMI (R = 0.731, P < 0.0001). The mean absolute error of AHI estimation was 7.26 events/h. Area under the receiver operating characteristic curve outcomes were 0.90, 0.87 and 0.93 for classifying patients with varying severity separated by the canonical thresholds of 5, 15 and 30 events/h respectively. The accuracy of classifying subjects to four classes (no, mild, moderate, and severe OSA) was 59.3 %. ConclusionAdditional audio-based snore features can improve the performance of non-obtrusive AHI estimation and OSA severity classification methods.

Diagnostic value of upper airway morphological data based on CT volume scanning combined with clinical indexes in children with obstructive sleep apnea syndrome.

Early diagnosis is important for treatment and prognosis of obstructive sleep apnea (OSA)in children. Polysomnography (PSG) is the gold standard for the diagnosis of OSA. However, due to various reasons, such as inconvenient implementation, less equipped in primary medical institutions, etc., it is less used in children, especially in young children. This study aims to establish a new diagnostic method with imaging data of upper airway and clinical signs and symptoms. In this retrospective study, clinical and imaging data were collected from children ≤10 years old who underwent nasopharynx CT scan(low-dose protocol)from February 2019 to June 2020,including 25 children with OSA and 105 non-OSA. The information of the upper airway (A-line; N-line; nasal gap; upper airway volume; upper and lower diameter, left and right diameter and cross-sectional area of the narrowest part of the upper airway) were measured in transaxial, coronal, and sagittal images. The diagnosis of OSA and adenoid size were given according to the guidelines and consensus of imaging experts. The information of clinical signs, symptoms, and others were obtained from medical records. According to the weight of each index on OSA, the indexes with statistical significance were screened out, then were scored and summed up. ROC analysis was performed with the sum as the test variable and OSA as the status variable to evaluate the diagnostic efficacy on OSA. The AUC of the summed scores (ANMAH score) of upper airway morphology and clinical index for the diagnosis of OSA was 0.984 (95% CI 0.964-1.000). When sum = 7 was used as the threshold (participants with sum>7 were considered to have OSA), the Youden's index reached its maximum at which point the sensitivity was 88.0%, the specificity was 98.1%, and the accuracy was 96.2%. The morphological data of the upper airway based on CT volume scan images combined with clinical indices have high diagnostic value for OSA in children; CT volume scanning plays a great guiding role in the selection of treatment scheme of OSA. It is a convenient, accurate and informative diagnostic method with a great help to improving prognosis. - Early diagnosis of OSA in children is very important for the treatment.- However, the traditional diagnostic gold-standard PSG is difficult to implement.- This study aims to explore convenient and reliable diagnostic methods for children.- A new diagnostic model was established combining CT with signs and symptoms.- The diagnostic method in this study is highly effective, informative, and convenient.

Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study.

Most existing automated sleep staging methods rely on multimodal data, and scoring a specific epoch requires not only the current epoch but also a sequence of consecutive epochs that precede and follow the epoch. We proposed and tested a convolutional neural network called SleepInceptionNet, which allows sleep classification of a single epoch using a single-channel electroencephalogram (EEG). SleepInceptionNet is based on our systematic evaluation of the effects of different EEG preprocessing methods, EEG channels, and convolutional neural networks on automatic sleep staging performance. The evaluation was performed using polysomnography data of 883 participants (937,975 thirty-second epochs). Raw data of individual EEG channels (ie, frontal, central, and occipital) and 3 specific transformations of the data, including power spectral density, continuous wavelet transform, and short-time Fourier transform, were used separately as the inputs of the convolutional neural network models. To classify sleep stages, 7 sequential deep neural networks were tested for the 1D data (ie, raw EEG and power spectral density), and 16 image classifier convolutional neural networks were tested for the 2D data (ie, continuous wavelet transform and short-time Fourier transform time-frequency images). The best model, SleepInceptionNet, which uses time-frequency images developed by the continuous wavelet transform method from central single-channel EEG data as input to the InceptionV3 image classifier algorithm, achieved a Cohen κ agreement of 0.705 (SD 0.077) in reference to the gold standard polysomnography. SleepInceptionNet may allow real-time automated sleep staging in free-living conditions using a single-channel EEG, which may be useful for on-demand intervention or treatment during specific sleep stages.

At-home sleep monitoring using generic ear-EEG.

A device comprising two generic earpieces with embedded dry electrodes for ear-centered electroencephalography (ear-EEG) was developed. The objective was to provide ear-EEG based sleep monitoring to a wide range of the population without tailoring the device to the individual. To validate the device ten healthy subjects were recruited for a 12-night sleep study. The study was divided into two parts; part A comprised two nights with both ear-EEG and polysomnography (PSG), and part B comprised 10 nights using only ear-EEG. In addition to the electrophysiological measurements, subjects filled out a questionnaire after each night of sleep. The subjects reported that the ear-EEG system was easy to use, and that the comfort was better in part B. The performance of the system was validated by comparing automatic sleep scoring based on ear-EEG with PSG-based sleep scoring performed by a professional trained sleep scorer. Cohen's kappa was used to assess the agreement between the manual and automatic sleep scorings, and the study showed an average kappa value of 0.71. The majority of the 20 recordings from part A yielded a kappa value above 0.7. The study was compared to a companioned study conducted with individualized earpieces. To compare the sleep across the two studies and two parts, 7 different sleeps metrics were calculated based on the automatic sleep scorings. The ear-EEG nights were validated through linear mixed model analysis in which the effects of equipment (individualized vs. generic earpieces), part (PSG and ear-EEG vs. only ear-EEG) and subject were investigated. We found that the subject effect was significant for all computed sleep metrics. Furthermore, the equipment did not show any statistical significant effect on any of the sleep metrics. These results corroborate that generic ear-EEG is a promising alternative to the gold standard PSG for sleep stage monitoring. This will allow sleep stage monitoring to be performed in a less obtrusive way and over longer periods of time, thereby enabling diagnosis and treatment of diseases with associated sleep disorders.

Feasibility of wearable devices and machine learning for sleep classification in children with Rett syndrome: A pilot study.

Sleep is vital to many processes involved in the well-being and health of children; however, it is estimated that 80% of children with Rett syndrome suffer from sleep disorders. Caregiver reports and questionnaires, which are the current method of studying sleep, are prone to observer bias and missed information. Polysomnography is considered the gold standard for sleep analysis but is labor and cost-intensive and limits the frequency of data collection for sleep disorder studies. Wearable digital health technologies, such as actigraphy devices, have shown potential and feasibility as a method for sleep analysis in Rett syndrome, but have not been validated against polysomnography. Furthermore, the collected accelerometer data has limitations due to the rigidity, periodic limb movement, and involuntary muscle contractions prevalent in Rett syndrome. Heart rate and electrodermal activity, along with other physiological signals, have been linked to sleep stages and can be utilized with machine learning to provide better resistance to noise and false positives than actigraphy. This research aims to address the gap in Rett syndrome sleep analysis by comparing the performance of a machine learning model utilizing both accelerometer data and physiological data features to the gold-standard polysomnography for sleep analysis in Rett syndrome. Our analytical validation pilot study ( = 7) found that using physiological and accelerometer features, our machine learning models can differentiate between awake, non-rapid eye movement sleep, and rapid eye movement sleep in Rett syndrome children with an accuracy of 85.1% when using an individual model. Additionally, this work demonstrates that it is feasible to use digital health technologies in Rett syndrome, even at a young age, without data loss or interference from repetitive movements that are characteristic of Rett syndrome.

COMPARISON OF SLEEP PARAMETERS FROM WRIST-WORN ACCELEROMETERS IN OLDER ADULTS

Abstract Researchers often prefer different accelerometers for measurement of sleep and physical activity (PA) in older adults, but wearing multiple accelerometers increases costs and participant burden. We tested the hypothesis that the Actiwatch-2, commonly used to measure sleep, and the ActiGraph GT9X Link, commonly used to measure PA, yield comparable sleep parameters among 331 participants (aged 71+/-14 years, 50.5% women) who wore them simultaneously for 7+/-1.6 nights in the BLSA. Compared to the Actiwatch-2, the ActiGraph estimated 50% greater wake after sleep onset, 89% longer wake bout length, 18% lower sleep fragmentation index (SFI), 5% lower total sleep time (TST) and 5% higher sleep efficiency. Yet, despite yielding different absolute values of TST and SFI, the devices generated similar between-person (relative) differences in TST and SFI. These devices may only be interchangeable for measurement of some relative sleep parameters in older adults. Studies with both devices and gold-standard polysomnography are needed.