Simultaneous Polysomnography Research Articles

Evaluating Somfit’s pulse arterial tonometry for detection of obstructive sleep apnoea

AbstractThis study evaluates the diagnostic accuracy of Somfit against polysomnography (PSG) for detecting obstructive sleep apnoea (OSA). Somfit is a wearable home-sleep monitoring device attached to the forehead, combining pulse arterial tonometry, oximetry, and actigraphy with sleep staging, arousals, and total sleep time (TST) derived from frontal neurological signals. Ninety-two participants suspected of having OSA were assessed using Somfit and simultaneous overnight PSG recordings at three Australian sites. Each PSG study was manually scored by three independent scorers. The reported statistics include standard measures of agreement between Somfit’s TST, Oxygen-Saturation Index (ODI), Apnoea–Hypopnea Index (AHI), and the average of those metrics from the three PSG scorers. The overall inter-scorer agreement was 76% (kappa = 0.772). TST, ODI, and AHI from Somfit were highly correlated with similar metrics from PSG (all r > 0.84, p < 0.001). Sensitivity, specificity, and accuracy were 90.5%, 75.0%, and 89.1% respectively, with a diagnostic odds ratio (DOR) of 28.5 for AHI ≥ 5. For AHI ≥ 15, sensitivity, specificity, and accuracy were 78.0%, 73.8%, and 76.1%, respectively, with a DOR of 9.99. For AHI ≥ 30, sensitivity, specificity, accuracy, and DOR were 72.4%, 90.5%, 84.8%, and 24.9, respectively. The area under the curve (AUC) at different PSG AHI cut-offs ranged between 0.86 and 0.93. Coupled with oximetry and EEG-based derivation of TST, Somfit’s performance is comparable to PSG in detecting OSA severity.

Drug induced sleep endoscopy and simultaneous polysomnography to predict the effectiveness of mandibular advancement device in obstructive sleep apnea treatment.

To evaluate whether mandibular advancement device therapy is recommended in patients affected by obstructive sleep apnea. In order to predict oral appliances therapy response, drug induced sleep endoscopy with cardio-respiratory polygraphy and mandibular advancement device simulator was carried out. Patients in which upper airway obstruction was resolved on all levels and AHI was normalized (< 5/h), were referred for oral appliance therapy. At 5 months follow up, a cardio-respiratory polygraphy with MAD was performed. 36 patients who have evidence of resolution of UA collapse and AHI below 5 events per hour, were referred for MAD therapy. At follow up, the mean AHI decreased from 29.1 ± 13.1 to 3.3/h ± 1.9 (p < 0.001). All the patients were responders. Combining the evaluation of drug induced sleep endoscopy and cardio-respiratory polygraphy data simultaneously during mandibular protrusion, has the potential to be a useful tool for prediction of MAD therapy response.

Exploring bispectral index as an alternative to polysomnography for long-term sleep monitoring in disorders of consciousness.

Sleep plays a crucial role in neuroplasticity and recovery from brain injury, particularly in disorders of consciousness (DoC), including unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). Traditional sleep monitoring methods like polysomnography (PSG) are complex and often impractical for long-term use in clinical settings. This study aimed to explore the utility of the Bispectral Index (BIS) as a more practical alternative for monitoring sleep patterns in DoC patients. We conducted simultaneous PSG and BIS monitoring on 38 DoC patients (19 UWS and 19 MCS). The study focused on analyzing sleep timing distribution, the effectiveness of BIS in differentiating sleep stages, and its correlation with consciousness levels. Our findings revealed that DoC patients exhibited irregular and fragmented sleep patterns, necessitating extended monitoring periods. The BIS effectively differentiated various sleep stages, with significant differences in BIS values observed across these stages. However, BIS values did not show significant differences between UWS and MCS patients, indicating that BIS primarily indicates wakefulness rather than cognitive awareness. DoC patients have disturbed sleep-wake cycles that require prolonged monitoring. BIS can well distinguish sleep stages in DoC patients, and the distribution of values is similar to that of normal subjects. However, BIS could not distinguish the level of consciousness of DoC patients. The study demonstrates the potential of BIS as a practical tool for long-term sleep monitoring in DoC patients, offering a less intrusive alternative to traditional methods.

A custom-built single-channel in-ear electroencephalography sensor for sleep phase detection: an interdependent solution for at-home sleep studies.

Sleep is vital for health. It has regenerative and protective functions. Its disruption reduces the quality of life and increases susceptibility to disease. During sleep, there is a cyclicity of distinct phases that are studied for clinical purposes using polysomnography (PSG), a costly and technically demanding method that compromises the quality of natural sleep. The search for simpler devices for recording biological signals at home addresses some of these issues. We have reworked a single-channel in-ear electroencephalography (EEG) sensor grounded to a commercially available memory foam earplug with conductive tape. A total of 14 healthy volunteers underwent a full night of simultaneous PSG, in-ear EEG and actigraphy recordings. We analysed the performance of the methods in terms of sleep metrics and staging. In another group of 14 patients evaluated for sleep-related pathologies, PSG and in-ear EEG were recorded simultaneously, the latter in two different configurations (with and without a contralateral reference on the scalp). In both groups, the in-ear EEG sensor showed a strong correlation, agreement and reliability with the 'gold standard' of PSG and thus supported accurate sleep classification, which is not feasible with actigraphy. Single-channel in-ear EEG offers compelling prospects for simplifying sleep parameterisation in both healthy individuals and clinical patients and paves the way for reliable assessments in a broader range of clinical situations, namely by integrating Level 3 polysomnography devices. In addition, addressing the recognised overestimation of the apnea-hypopnea index, due to the lack of an EEG signal, and the sparse information on sleep metrics could prove fundamental for optimised clinical decision making.

Performance of Wrist-Worn Pulse Oximeter for the Screening of Obstructive Sleep Apnea.

A sleep apnea monitor (BM2000A) is a wrist-worn device that measures oxygen saturation and pulse rate during sleep. This study aimed to evaluate the efficacy of the watch-like BM2000A for screening obstructive sleep apnea (OSA). 102 patients complaining of sleep breathing disorders were included; 81% were men and 19% were women. All participants underwent overnight simultaneous polysomnography (PSG) and BM2000A sleep monitoring. The number of apneas and hypopneas, apnea-hypopnea index (AHI), percentage of time spent with oxygen saturation under 90%, average oxygen saturation, lowest oxygen saturation, and duration of sleep were computed by the BM2000A and PSG. Then, these parameters were compared to validate the BM2000A. All parameters, measured with BM2000A, had a good correlation (r ≥ 0.6, p < 0.0001) with PSG-derived indexes, except for sleep time (r = 0.19, p = 0.061) and hypopnea index (r = 0.4, p < 0.0001). AHI had the strongest correlation (r = 0.87, p < 0.0001). The mean difference between AHI values calculated with PSG and wrist-worn pulse oximeter (WPO) was -17.66 events/h (95% CI: -50.39 to 15.06). In AHI ≥ 5, BM2000A had 90.7% sensitivity, 100% specificity, 91.2% accuracy, and 0.994 area under the curve. Using AHI ≥ 5, ≥ 15, and ≥30 as the screening criteria, optimal WPO-AHI cutoffs to improve the screening accuracy were 3.10, 8.92, and 13.05. BM2000A-derived results properly correlate with PSG and can provide OSA screening with good sensitivity and specificity.

Duration of N1 sleep is a factor for excessive daytime sleepiness in epilepsy patients with interictal epileptiform discharges: A polysomnographic study

PurposeThis study aimed to identify the occurrence of excessive daytime sleepiness (EDS) in epilepsy patients with interictal epileptiform discharges and to explore the impact of interictal sleep architecture and sleep-related events on EDS. MethodsThis study included 101 epilepsy patients with interictal epileptiform discharges (IED) and 100 control patients who underwent simultaneous polysomnography and video ambulatory electroencephalography for >7 h throughout a single night. Multiple sleep latency tests were used to assess EDS. Comorbid EDS was present in 25 and 11 patients in the IED epilepsy and control groups, respectively. In addition, univariate and multivariate logistic regression analyses were performed to explore the factors influencing EDS. ResultsThe epilepsy group had a higher prevalence of comorbid EDS and shorter R sleep duration. Univariate logistic regression analysis indicated that an increased risk of EDS may be associated with prolonged N1 sleep duration, higher arousal index, lower mean saturation (mSaO2), higher oxygen desaturation index (ODI), and duration of wake after sleep onset (WASO). Multivariate logistic regression analysis revealed that N1 sleep duration was significantly correlated with EDS. ConclusionIn epilepsy patients with IED, the arousal index, mSaO2, ODI, and duration of WASO were weakly correlated with EDS, and the duration of N1 sleep demonstrated a significant positive correlation with EDS, which requires further research.

Divergent Associations of Slow-Wave Sleep versus Rapid Eye Movement Sleep with Plasma Amyloid-Beta.

Recent evidence shows that during slow-wave sleep (SWS), the brain is cleared from potentially toxic metabolites, such as the amyloid-beta protein. Poor sleep or elevated cortisol levels can worsen amyloid-beta clearance, potentially leading to the formation of amyloid plaques, a neuropathological hallmark of Alzheimer disease. Here, we explored how nocturnal neural and endocrine activity affects amyloid-beta fluctuations in the peripheral blood. We acquired simultaneous polysomnography and all-night blood sampling in 60 healthy volunteers aged 20-68 years. Nocturnal plasma concentrations of amyloid-beta-40, amyloid-beta-42, cortisol, and growth hormone were assessed every 20 minutes. Amyloid-beta fluctuations were modeled with sleep stages, (non)oscillatory power, and hormones as predictors while controlling for age and participant-specific random effects. Amyloid-beta-40 and amyloid-beta-42 levels correlated positively with growth hormone concentrations, SWS proportion, and slow-wave (0.3-4Hz) oscillatory and high-band (30-48Hz) nonoscillatory power, but negatively with cortisol concentrations and rapid eye movement sleep (REM) proportion measured 40-100 minutes previously (all t values > |3|, p values < 0.003). Older participants showed higher amyloid-beta-40 levels. Slow-wave oscillations are associated with higher plasma amyloid-beta levels, whereas REM sleep is related to decreased amyloid-beta plasma levels, possibly representing changes in central amyloid-beta production or clearance. Strong associations between cortisol, growth hormone, and amyloid-beta presumably reflect the sleep-regulating role of the corresponding releasing hormones. A positive association between age and amyloid-beta-40 may indicate that peripheral clearance becomes less efficient with age. ANN NEUROL 2024;96:46-60.

Validation of sleep-staging accuracy for an in-home sleep electroencephalography device compared with simultaneous polysomnography in patients with obstructive sleep apnea

Efforts to simplify standard polysomnography (PSG) in laboratories, especially for obstructive sleep apnea (OSA), and assess its agreement with portable electroencephalogram (EEG) devices are limited. We aimed to evaluate the agreement between a portable EEG device and type I PSG in patients with OSA and examine the EEG-based arousal index’s ability to estimate apnea severity. We enrolled 77 Japanese patients with OSA who underwent simultaneous type I PSG and portable EEG monitoring. Combining pulse rate, oxygen saturation (SpO2), and EEG improved sleep staging accuracy. Bland–Altman plots, paired t-tests, and receiver operating characteristics curves were used to assess agreement and screening accuracy. Significant small biases were observed for total sleep time, sleep latency, awakening after falling asleep, sleep efficiency, N1, N2, and N3 rates, arousal index, and apnea indexes. All variables showed > 95% agreement in the Bland–Altman analysis, with interclass correlation coefficients of 0.761–0.982, indicating high inter-instrument validity. The EEG-based arousal index demonstrated sufficient power for screening AHI ≥ 15 and ≥ 30 and yielded promising results in predicting apnea severity. Portable EEG device showed strong agreement with type I PSG in patients with OSA. These suggest that patients with OSA may assess their condition at home.

O007 Validation of an under-mattress sleep tracker to estimate sleep and wake during day and night sleep opportunities

Abstract Introduction Consumer sleep trackers are useful for tracking daily sleep, particularly in shift-workers, yet large, multi-night validations against direct sleep measures are lacking. Conducted as one of the largest sleep tracker validation studies to date, this study evaluated performance of an under-mattress sensor (Withings Sleep Analyzer [WSA]) to classify sleep and wake versus polysomnography (PSG) in a multi-night laboratory protocol with both day and night sleep opportunities. Methods 27 healthy sleepers attended the laboratory twice, for 8 consecutive days (48% male, mean[SD] age=27[9]years). Participants underwent simultaneous PSG and WSA recordings for the initial night-time sleep (22:00-07:00) and 6 subsequent daytime sleeps (10:00-19:00) during simulated night-shift work. Epoch-by-epoch analysis of the 252 nights tested accuracy, sensitivity, and specificity for the first night-time sleep opportunity versus subsequent daytime sleeps. Sleep duration estimates were compared between WSA and PSG. Results Across all recordings, the WSA showed 83% sleep-wake classification accuracy, 87% sensitivity to sleep and 25% specificity to wake. The WSA significantly overestimated sleep duration versus PSG (25[65]minutes, p&amp;lt;0.05). Accuracy and specificity were higher for night versus day sleeps (88% and 38% versus 81% and 18% respectively, all p&amp;lt;0.05), while sensitivity (90% versus 88%) did not significantly differ. Discussion The WSA was moderately accurate compared to PSG, and comparable to other movement-based sleep trackers. The WSA was more accurate at classifying sleep and wake during night sleep opportunities compared to daytime sleeps. This differential performance is likely due to poorer sleep quality commonly observed during daytime sleep that is harder to accurately classify.

Transient intracranial pressure elevations (B waves) are associated with sleep apnea

BackgroundRepetitive transient intracranial pressure waveform elevations up to 50 mmHg (ICP B-waves) are often used to define pathological conditions and determine indications for ICP-reducing treatment. We recently showed that nocturnal transient ICP elevations are present in patients without structural brain lesions or hydrocephalus in whom they are associated with sleep apnea. However, whether this signifies a general association between ICP macropatterns and sleep apnea remains unknown.MethodsWe included 34 patients with hydrocephalus, or idiopathic intracranial hypertension (IIH), who were referred to the Neurosurgical Department, Copenhagen, Denmark, from 2017 to 2021. Every patient underwent diagnostic overnight ICP monitoring for clinical indications, with simultaneous polysomnography (PSG) sleep studies. All transient ICP elevations were objectively quantified in all patients. Three patients were monitored with continuous positive airway pressure (CPAP) treatment for an additional night.ResultsAll patients had transient ICP elevations associated with sleep apnea. The mean temporal delay from sleep apnea to transient ICP elevations for all patients was 3.6 s (SEM 0.2 s). Ramp-type transient ICP elevations with a large increase in ICP were associated with rapid eye movement (REM) sleep and sinusoidal-type elevations with non-REM (NREM) sleep. In three patients treated with CPAP, the treatment reduced the number of transient ICP elevations with a mean of 37%. CPAP treatment resulted in insignificant changes in the average ICP in two patients but elevated the average ICP during sleep in one patient by 5.6 mmHg.ConclusionThe findings suggest that sleep apnea causes a significant proportion of transient ICP elevations, such as B-waves, and sleep apnea should be considered in ICP evaluation. Treatment of sleep apnea with CPAP can reduce the occurrence of transient ICP elevations. More research is needed on the impact of slow oscillating mechanisms on transient ICP elevations during high ICP and REM sleep.

Contactless sleep monitoring using the Sonomat in children with Down syndrome

BackgroundChildren with Down syndrome (DS) require multiple sleep studies throughout childhood to diagnose and monitor sleep disordered breathing (SDB). Previous research suggests sensors applied during polysomnography (PSG) are poorly tolerated by children with DS. The Sonomat is a contactless device previously validated in typically developing children, and adult populations. Our study aimed to compare simultaneous Sonomat and PSG recordings in children with DS to determine the suitability of the Sonomat for use in this population. MethodsFifty children with DS undergoing diagnostic PSG were recruited from the Queensland Children's Hospital sleep laboratory. Agreement for sleep and respiratory parameters were assessed using concordance correlation coefficients (CCC), while detection and classification of SDB were assessed using indicators such as sensitivity, specificity, likelihood ratios, and receiver operator characteristic curves. ResultsComparison of parameters including the apnea-hypopnea index (CCC=89%; 95%CI 76, 93), and obstructive events index (CCC=74%; 95%CI 44, 88) showed reasonable agreement between Sonomat and PSG. The Sonomat showed outstanding ability to differentiate between the presence and absence of SDB (area under the curve [AUC]=0.97; 95%CI 0.93, 1.00), though we are uncertain of its ability to classify SDB profile and severity due to sample size limitations resulting in wide confidence intervals. ConclusionsThe easy-to-use, non-invasive nature of the Sonomat make it ideal for use in the screening of SDB in children with DS. Further data are needed to determine its suitability for classifying SDB profile and severity, including within the home, and for its use in other neurodevelopmental groups.

Performance evaluation of Fitbit Charge 3 and actigraphy vs. polysomnography: Sensitivity, specificity, and reliability across participants and nights

Goal and aimsCompare the accuracy and reliability of sleep/wake classification between the Fitbit Charge 3 and the Micro Motionlogger actigraph when applying either the Cole-Kripke or Sadeh scoring algorithms. Accuracy was established relative to simultaneous Polysomnography recording.Focus technology: Fitbit Charge 3 and actigraphy.Reference technology: Polysomnography. SampleTwenty-one university students (10 females). DesignSimultaneous Fitbit Charge 3, actigraphy, and polysomnography were recorded over 3 nights at the participants’ homes. Core analyticsTotal sleep time, wake after sleep onset, sensitivity, specificity, positive predictive value, and negative predictive value. Additional analytics and exploratory analysesVariability of specificity and negative predictive value across subjects and across nights. Core outcomesFitbit Charge 3 and actigraphy using the Cole-Kripke or Sadeh algorithms exhibited similar sensitivity in classifying sleep segments relative to polysomnography (sensitivity of 0.95, 0.96, and 0.95, respectively). Fitbit Charge 3 was significantly more accurate in classifying wake segments (specificity of 0.69, 0.33, and 0.29, respectively). Fitbit Charge 3 also exhibited significantly higher positive predictive value than actigraphy (0.99 vs. 0.97 and 0.97, respectively) and a negative predictive value that was significantly higher only relative to the Sadeh algorithm (0.41 vs. 0.25, respectively). Important additional outcomesFitbit Charge 3 exhibited significantly lower standard deviation in specificity values across subjects and negative predictive value across nights. Core conclusionThis study demonstrates that Fitbit Charge 3 is more accurate and reliable in identifying wake segments than the examined FDA-approved Micro Motionlogger actigraphy device. The results also highlight the need to create devices that record and save raw multi-sensor data, which are necessary for developing open-source sleep or wake classification algorithms.

The Belun sleep platform to diagnose obstructive sleep apnea in patients with hypertension and high cardiovascular risk.

Current hypertension guidelines recommend that at-risk individuals be screened for obstructive sleep apnea (OSA). The Belun Ring is a wearable OSA diagnostic device worn on the palmar side of the proximal phalanx of the index finger. We recruited 129 participants (age: 60 ± 8 years, male sex: 88%, BMI: 27 ± 4 kg/m2) with hypertension and high cardiovascular risk for a simultaneous polysomnography and Belun Ring monitoring for one night. Epworth Sleepiness Scale score more than 10 was detected in 27 (21.0%) participants. In the 127 participants who completed the study, the apnea-hypopnea index (AHI) derived from polysomnography was 18.1 (interquartile range: 33.0) events/h and that derived from the Belun Ring was 19.5 (interquartile range: 23.3) events/h [intraclass correlation coefficient: 0.882, 95% confidence interval (95% CI): 0.837-0.916]. A Bland-Altman plot showed the difference between the Belun Ring and polysomnography AHIs to be -1.3 ± 10.4 events/h. Area under the receiver operating characteristic for the Belun Ring AHI was 0.961 (95% CI: 0.932-0.990, P < 0.001). When the Belun Ring AHI of at least 15 events/h was used to diagnose OSA, the sensitivity, specificity, positive predictive value, and negative predictive value were 95.7, 77.6, 85.3, and 93.8%, respectively. The overall accuracy was 87.4%. The Cohen's kappa agreement was 0.74 ± 0.09 (P < 0.001). Similar results were obtained when the oxygen desaturation index was used to diagnose OSA. A high prevalence of OSA was detected in patients with hypertension and high cardiovascular risk. The Belun Ring is a reliable device for OSA diagnosis similar to polysomnography.Graphical abstract: http://links.lww.com/HJH/C180.

A region-specific modulation of sleep slow waves on interictal epilepsy markers in focal epilepsy.

Sleep strongly activates interictal epileptic activity through an unclear mechanism. We investigated how scalp sleep slow waves (SSWs), whose positive and negative half-waves reflect the fluctuation of neuronal excitability between the up and down states, respectively, modulate interictal epileptic events in focal epilepsy. Simultaneous polysomnography was performed in 45 patients with drug-resistant focal epilepsy during intracranial electroencephalographic recording. Scalp SSWs and intracranial spikes and ripples (80-250 Hz) were detected; ripples were classified as type I (co-occurring with spikes) or type II (occurring alone). The Hilbert transform was used to analyze the distributions of spikes and ripples in the phases of SSWs. Thirty patients with discrete seizure-onset zone (SOZ) and discernable sleep architecture were included. Intracranial spikes and ripples accumulated around the negative peaks of SSWs and increased with SSW amplitude. Phase analysis revealed that spikes and both ripple subtypes in SOZ were similarly facilitated by SSWs exclusively during down state. In exclusivelyirritative zones outside SOZ(EIZ), SSWs facilitated spikes and type I ripples across a wider range of phases and to a greater extentthanthoseinSOZ.Thetype II andtypeIripples inEIZweremodulatedbySSWsin different patterns. Ripples in normal zones decreased specifically during the up-to-down transition and then increased after the negative peak of SSW, with a characteristically high post-/pre-negative peak ratio. SSWs modulate interictal events in an amplitude-dependent and region-specific pattern. Pathological ripples and spikes were facilitated predominantly during the cortical down state. Coupling analysis of SSWs could improve the discrimination of pathological and physiological ripples and facilitate seizure localization.

Interaction between alcohol consumption and use of peripheral arterial tone home sleep apnea tests for sleep apnea evaluation

Objective/BackgroundHome sleep apnea tests utilizing peripheral arterial tone (PAT HSAT) detect sleep disordered breathing by measuring various physiologic measures including changes in arterial volume in the finger. Validation tests comparing PAT HSAT to simultaneous polysomnography (PSG) have demonstrated a high correlation. Alcohol increases peripheral vasodilation, which may alter arterial tone in the finger. Validation studies have not evaluated for an interaction between alcohol consumption and PAT HSAT measures. Patients/Methods: We describe an in-depth evaluation of a 53-year-old man who consumes alcohol on nightly basis. He underwent a series of 5 diagnostic studies under different conditions: three PAT HSATs (two nights with and another without alcohol) and two polysomnograms (one night with and another without alcohol). ResultsObstructive sleep apnea (OSA) was found on both polysomnograms but only on the PAT HSAT without alcohol, raising the possibility of two false negative PAT HSAT results after alcohol consumption. ConclusionsThis report demonstrates the need for further investigations into the performance of PAT HSATs with and without alcohol. In the meantime we recommend that testing be done without alcohol and over the course of multiple nights.

An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device.

BackgroundThe rapid advancement in wearable solutions to monitor and score sleep staging has enabled monitoring outside of the conventional clinical settings. However, most of the devices and algorithms lack extensive and independent validation, a fundamental step to ensure robustness, stability, and replicability of the results beyond the training and testing phases. These systems are thought not to be feasible and reliable alternatives to the gold standard, polysomnography (PSG).Materials and methodsThis validation study highlights the accuracy and precision of the proposed heart rate (HR)-based deep-learning algorithm for sleep staging. The illustrated solution can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-s epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n = 994 participants, 994 recordings) and a proprietary dataset of ECG recordings (Z3Pulse, n = 52 participants, 112 recordings) collected with a chest-worn, wireless sensor and simultaneous PSG collection using SOMNOtouch.ResultsWe evaluated the performance of the models in both datasets in terms of Accuracy (A), Cohen’s kappa (K), Sensitivity (SE), Specificity (SP), Positive Predictive Value (PPV), and Negative Predicted Value (NPV). In the CinC dataset, the highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect scoring, while a significant decrease of performance by age was reported across the models. In the Z3Pulse dataset, the highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment.ConclusionThe results of the validation procedure demonstrated the feasibility of accurate HR-based sleep staging. The combination of the proposed sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution deployable in the home environment and robust across age, sex, and AHI scores.

Increased rates of brain protein synthesis during [N1,N2] sleep: L-[1-11C]leucine PET studies in human subjects

During sleep, reduced brain energy demands provide an opportunity for biosynthetic processes like protein synthesis. Sleep is required for some forms of memory consolidation which requires de novo protein synthesis. We measured regional cerebral protein synthesis rates (rCPS) in human subjects to ascertain how rCPS is affected during sleep. Subjects underwent three consecutive L-[1-11C]leucine PET scans with simultaneous polysomnography: 1. rested awake, 2. sleep-deprived awake, 3. sleep. Measured rCPS were similar across the three conditions. Variations in sleep stage times during sleep scans were used to estimate rCPS in sleep stages under the assumption that measured rCPS is the weighted sum of rCPS in each stage, with weights reflecting time and availability of [11C]leucine in that stage. During sleep scans, subjects spent most of the time in N2, N3, and awake and very little time in N1 and REM; rCPS in N1 and REM could not be reliably estimated. When stages N1 and N2 were combined [N1,N2], estimates of rCPS were more robust. In selective regions, estimated rCPS were statistically significantly higher (30–39%) in [N1,N2] compared with N3; estimated rCPS in N3 were similar to values measured in sleep-deprived awake scans. Results indicate increased rates of protein synthesis linked to [N1,N2] sleep.

Comparison of Ring Pulse Oximetry Using Reflective Photoplethysmography and PSG in the Detection of OSA in Chinese Adults: A Pilot Study.

ObjectiveA novel ring-worn oximeter (Circul) uses reflective photoplethysmography and automated signal processing to calculate oxygen desaturations. We evaluated the ability of Circul to detect obstructive sleep apnea in Chinese adults.MethodsWe recruited 207 Chinese Han subjects: 70% males, mean age 48.2±14.7 years, mean BMI 27.6±4.8 kg/m2 and mean AHI 28.6±25.2 events/h. All participants underwent simultaneous polysomnography (PSG) and Circul testing in a sleep laboratory. Oxygen desaturation index (ODI), mean oxygen saturation (MSpO2), cumulative time at SpO2<90% (CT90), cumulative percentage of sleep time spent with SpO2<90% (CT90/TST) were derived and compared for the Circul and the PSG.ResultsThe ODI was 25.3±24.5 events/h using PSG and 22.2±24.5 events/h using Circul (P<0.0001), with an intraclass correlation coefficient (ICC) of 0.884. CT90 and CT90/TST between the two methods were not different; the MSpO2 level calculated by PSG was slightly lower than Circul, 95.0% (93.0–96.0%) vs 95.3% (93.9–96.6%), P<0.0001. Circul-ODI had a good correlation (r=0.91, p<0.0001) and close agreement with PSG-AHI (Bland-Altman analysis: Mean Difference 6.4, 95% CI −14.8 to 27.5 events/h). Using a threshold of AHI ≥5 events/h, the Circul had 87% sensitivity, 83% specificity, 5.09 positive likelihood ratio (LR+), 86% accuracy, and 0.929 area under the curve (AUC).ConclusionCircul ring pulse oximetry can detect OSA with reasonable reliability. The Circul system is a reliable and comfortable choice for OSA assessment.

0096 Sleep Apnea Diagnosis using Tracheal Signals and Oximetry

Abstract Introduction Diagnosing sleep apnea requires detection of apneas and hypopneas during sleep either via in-laboratory overnight polysomnography (PSG) or portable in-home sleep apnea testing (HSAT). While PSG is the optimal method, it is expensive, inconvenient and often inaccessible for patients. Although HSATs are more convenient and less expensive than PSG, they are not as accurate and have relatively high failure rates because of the nature of the sensors used to measure respiratory variables. We have developed a HSAT, the Patch, that is unique in that it is very simple and reliable because it measures respiratory variables using tracheal motion and sound combined with oximetry to detect respiratory events. In this study we tested the ability of the Patch to detect respiratory events versus simultaneous PSG. Methods Participants were adults with a suspected sleep disorder referred to the sleep laboratory at Toronto Rehabilitation Institute for PSG. Simultaneous to the PSG, the Patch, consisting of a module containing a microphone and a 3-D accelerometer that was affixed to participants’ suprasternal notch, and a finger oximeter. After filtering the tracheal signals, the envelope of the tracheal motions in the cranial and postero-anterior directions and tracheal sound envelope were extracted. Along with tracheal features, the amplitude and slopes of oxygen desaturations were also extracted and, were fed into a supervised deep neural network model to detect apneas and hypopneas. The total number of detected events was divided by total estimated sleep time to estimate the apnea-hypopnea index (AHI). The performance of the model in diagnosing sleep apnea was evaluated by sensitivity (AHI≥15) and specificity (AHI&amp;lt;15). The relationship between the estimated AHI and PSG-based AHI was quantified using Pearson correlation. Results Ninety-nine participants (42 females, age: 48±16 years, body mass index: 29.2±5.2 kg/m2, and AHI: 15.8±19.4 events/hour) completed the study. We found that the Patch had 88.6% sensitivity and 89.1% specificity for diagnosing sleep apnea. Strong agreement was observed between the estimated and reference AHI values (r = 0.92, p &amp;lt; 0.001). Conclusion The Patch is a novel, robust and convenient portable device that provides an accurate means of detecting and quantifying sleep apnea. It has the potential to provide reliable home-based sleep apnea monitoring. Support (If Any) Funded by Bresotec Inc.

Study of the Agreement of the Apnea–Hypopnea Index Measured Simultaneously by Pressure Transducer via Respiratory Polygraphy and by Thermistor via Polysomnography in Real Time with the Same Individuals

Background: Obstructive sleep apnea (OSA) is a common disorder and can lead to many severe complications; however, the majority of patients remain undiagnosed. Although polysomnography (PSG) remains the gold standard of diagnosis, it is usually uncomfortable and costly for patients. Purpose: The study aims to assess the agreement of the AHI measured by polygraphy (PG) (Philips Alice NightOne) with that of polysomnography (Philips Alice PDx) simultaneously recorded in-lab. Methods: A total of 11 voluntary participants over 18 years old underwent one night of simultaneous PSG and PG recording in sleep laboratories. Studied parameters (AHI, OAI, CAI, MAI, and minSpO2) were analyzed and reported by the Philips Sleepware G3 software. PSG and PG results were scored by qualified staff. Results: In terms of AHI, the mean AHI derived from PG was different from that of PSG—7.78 and 2.37 events/h, respectively. A Bland–Altman analysis of the AHI on PSG versus PG showed a mean difference of 5.41; limits of agreement (equal to ±2 standard deviations) were from −6.74 to 17.56. The Bland–Altman analysis showed a slight difference between the two methods, with a mean difference of −0.12 events/h in CAI, 1.35 events/h in OAI, and 0.42 events/h in MAI. Conclusions: In the population with a low suspicion of OSA, the PG showed a low agreement with the simultaneous PSG in the sleep lab. Therefore, PG should only be used as a screening method. Further studies with sufficient sensors in the expanded populations of OSA are needed.