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Ultra-wideband radar application for sleep breathing monitoring is hampered by the difficulty of obtaining breathing signals for non-stationary subjects. This occurs due to imprecise signal clutter removal and poor body movement removal algorithms for extracting accurate breathing signals. Therefore, this paper proposed a Sleep Breathing Detection Algorithm (SBDA) to address this challenge. First, SBDA introduces the combination of variance feature with Discrete Wavelet Transform (DWT) to tackle the issue of clutter signals. This method used Daubechies wavelets with five levels of decomposition to satisfy the signal-to-noise ratio in the signal. Second, SBDA implements a curve fit based sinusoidal pattern algorithm for detecting periodic motion. The measurement was taken by comparing the R-square value to differentiate between chest and body movements. Last but not least, SBDA applied the Ensemble Empirical Mode Decomposition (EEMD) method for extracting breathing signals before transforming the signal to the frequency domain using Fast Fourier Transform (FFT) to obtain breathing rate. The analysis was conducted on 15 subjects with normal and abnormal ratings for sleep monitoring. All results were compared with two existing methods obtained from previous literature with Polysomnography (PSG) devices. The result found that SBDA effectively monitors breathing using IR-UWB as it has the lowest average percentage error with only 6.12% compared to the other two existing methods from past research implemented in this dataset.

Abstract Video-polysomnography (v-PSG) is essential for diagnosing rapid eye movement (REM) sleep behavior disorder (RBD). Although there are current American Academy of Sleep Medicine standards to diagnose RBD, several aspects need to be addressed to achieve harmonization across sleep centers. Prodromal RBD is a stage in which symptoms and signs of evolving RBD are present, but do not yet meet established diagnostic criteria for RBD. However, the boundary between prodromal and definite RBD is still unclear. As a common effort of the Neurophysiology Working Group of the International RBD Study Group, this manuscript addresses the need for comprehensive and unambiguous v-PSG recommendations to diagnose RBD and identify prodromal RBD. These include: (1) standardized v-PSG technical settings; (2) specific considerations for REM sleep scoring; (3) harmonized methods for scoring REM sleep without atonia; (4) consistent methods to analyze video and audio recorded during v-PSGs and to classify movements and vocalizations; (5) clear v-PSG guidelines to diagnose RBD and identify prodromal RBD. Each section follows a common template: The current recommendations and methods are presented, their limitations are outlined, and new recommendations are described. Finally, future directions are presented. These v-PSG recommendations are intended for both practicing clinicians and researchers. Classification and quantification of motor events, RBD episodes, and vocalizations are however intended for research purposes only. These v-PSG guidelines will allow collection of homogeneous data, providing objective v-PSG measures and making future harmonized multicentric studies and clinical trials possible.

Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages can help detect neurological disorders accurately as well. In this study, a flexible deep learning model is proposed using raw PSG signals. A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages. The performance of the system is evaluated using two public databases (sleep-edf and sleep-edfx). The developed model yielded the highest accuracies of 98.06%, 94.64%, 92.36%, 91.22%, and 91.00% for two to six sleep classes, respectively, using the sleep-edf database. Further, the proposed model obtained the highest accuracies of 97.62%, 94.34%, 92.33%, 90.98%, and 89.54%, respectively for the same two to six sleep classes using the sleep-edfx dataset. The developed deep learning model is ready for clinical usage, and can be tested with big PSG data.

Study Objectives: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of 2 levels of OSA severity (ie, moderate-to-severe and severe OSA) in accordance with clinical practice standards. Methods: We conducted a prospective, simultaneous study using a wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDI PSG_TST ), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDI PSG_TST and apnea-hypopnea index, results obtained through PSG. Cut-off thresholds for RDI PSG_TST were determined using Youden’s index, and multiclass classification was performed, after which the results were compared. Results: A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between apnea-hypopnea index and RDI PSG_TST were identified. In terms of the agreement between the 2 devices, the average difference between PSG-based apnea-hypopnea index and radar-based RDI PSG_TST was 0.59 events/h, and 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDI PSG_TST : 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDI PSG_TST : 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. Conclusions: The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work. Citation: Lin S-Y, Tsai C-Y, Majumdar A, et al. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med . 2024;20(8):1267–1277.

Study Objectives: Obstructive sleep apnea (OSA) is a chronic condition that is characterized by recurrent pauses in breathing during sleep causing intermittent hypoxia. The main factor responsible for oxygen metabolism homeostasis is hypoxia-inducible factor 1 (HIF-1), comprised of 2 subunits: α (oxygen sensitive) and β. The aim of the study was to investigate the HIF-1α serum protein level and mRNA HIF-1α expression in patients with OSA and a healthy control group and determine their evening-morning variation and association with polysomnography parameters. Methods: Eighty-four individuals were enrolled in the study. All patients underwent polysomnography examination and based on the results were divided into 2 groups: OSA group (n = 60) and control group (n = 24). Peripheral blood was collected in the evening before and in the morning after the polysomnography. HIF-1α expression was evaluated on protein in blood serum and mRNA level in peripheral blood leukocytes. Results: HIF-1α serum protein concentration was higher in patients with OSA compared with control patients in both the evening (1,490.1 vs. 727.0 pg/mL; P < .001) and the morning (1,368.9 vs. 702.1 pg/mL; P < .001) samples. There was no difference between evening and morning HIF-1α serum protein level in either group. No differences were observed in HIF-1α mRNA expression between the OSA and control group. Additionally, evening and morning HIF-1α serum protein level correlated with number of desaturations during sleep ( r = .384, P < .001 and r = .433, P < .001, respectively). Conclusions: Observed differences in HIF-1α serum protein level between the OSA and the control groups without difference between evening and morning measurements suggest chronic increase in this protein concentration by intermittent nocturnal hypoxia in OSA. Citation: Gabryelska A, Szmyd B, Szemraj J, Stawski R, Sochal M, Białasiewicz P. Patients with obstructive sleep apnea present with chronic upregulation of serum HIF-1α protein. J Clin Sleep Med . 2020;16(10):1761–1768.

Purpose: Insufficient sleep is common and under-reported, linked to increased health risks. Many individuals seek alternatives to conventional medications, which often have adverse side effects. Patients and Methods: This monocentric, single-arm, open-label exploratory study (Reg. No: NCT05748574) evaluated a granulate formulation containing 75 mg extract of the fresh herb of Lactuca sativa, 190 mg Melissa officinalis, 120 mg L- Tryptophan, and 60 mg Magnesium in healthy adults with sleep disturbances. Conducted in Germany in 2023, 50 subjects consumed the formula nightly for 14 days. Outcomes were assessed via diaries, questionnaires, cognitive tests, wearables, saliva samples, and polysomnography (PSG) in a 10-subject subgroup. Statistical analysis compared pre- and post-treatment differences. Results: Nightly awakenings reduced by 31% (p < 0.001) and early morning awakenings by 16% (p < 0.001). PSG data indicated a 28% increase in deep sleep (N3&N4, p > 0.05), a 70% rise in stage N4 (p = 0.042) and an 18% reduction in REM sleep (p > 0.05). The Apnea-Hypopnea index decreased by 26% (p = 0.11). Sleep quality (\"Sleep questionnaire\" SF-B/R index, primary outcome) improved by 14% (p = 0.003), with a 37% improvement in highly anxious individuals (p [less than or equal to] 0.001). Restedness increased by 22% in week 1 and 28% in week 2 (p [less than or equal to] 0.001). Psychological tension dropped by 21% and up to 29% (p < 0.001). Daytime performance indicators included a 13% reduction in sleepiness and a 23% improvement in mood (p [less than or equal to] 0.014). Executive function showed a 13% improvement (p [less than or equal to] 0.001) on computerized tests (COMPASS). Findings from wearables, sleep quantity, and salivary biomarkers yielded an inconsistent picture. Adherence was high, with no serious adverse events reported. Conclusion: The formulation was associated with observed improvements in subjective sleep quality--particularly among anxious individuals--as well as well-being and daytime function. Further confirmation through randomized placebo-controlled studies is warranted to further prove causality. Keywords: herbal supplement, polysomnography, PSG, insomnia, sleep quality, clinical trial, anxiety-related sleep disturbance

Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet’s cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen’s Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen’s Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.

Study Objectives: Self-reported perception of sleep often differs from objective sleep study measures, but factors predicting the discrepancy between self-reported and objective sleep parameters are controversial, and a comparison of laboratory vs ambulatory polysomnography (PSG) is lacking. Methods: We retrospectively analyzed PSGs conducted between 2012 and 2016. Linear regression was applied to predict the discrepancy between self-reported and objective sleep parameters (total sleep time, sleep efficiency, sleep latency, using age, sex, arousal index, type of sleep disorder, and PSG type [laboratory vs ambulatory] as regressors). Results: A total of 303 PSGs were analyzed (49% women, median age 48 years), comprising patients with insomnia (32%), sleep-related breathing disorders (27%), sleep-related movement disorders (15%), hypersomnia/narcolepsy (14%), and parasomnias (12%). Sleep disorder was the best predictor of discrepancy between self-reported and objective total sleep time, and patients with insomnia showed higher discrepancy values compared to all other patient groups ( P < .001), independent of age and PSG type ( P > .05). Contributory effects for higher discrepancy values were found for lower arousal index. Patients with insomnia underestimated both total sleep time (median discrepancy: 46 minutes, P < .001) and sleep efficiency (median discrepancy: 11%, P < .001). No significant predictor for discrepancy of sleep latency was found. Conclusions: Misperception of sleep duration and efficiency is common in sleep lab patients, but most prominent in insomnia, independent of age, sex, or laboratory vs ambulatory recording setting. This underlines the role of PSG in patients with a clinical diagnosis of insomnia and its use in cognitive behavioral therapy. Citation: Trimmel K, Eder HG, Böck M, Stefanic-Kejik A, Klösch G, Seidel S. The (mis)perception of sleep: factors influencing the discrepancy between self-reported and objective sleep parameters. J Clin Sleep Med . 2021;17(5):917–924.