The Accuracy and Reliability of Sleep Staging and Sleep Biomarkers in Patients with Isolated Rapid Eye Movement Sleep Behavior Disorder

Clinical validation of a wireless patch-based polysomnography system

STUDY OBJECTIVES

Onera Health has developed the first wireless, patch-based, type-II polysomnography (PSG) system, the Onera Sleep Test System, to allow studies to be performed unattended at the patient's home or in any bed at a medical facility. The goal of this multicenter study was to validate data collected from the patch-based PSG to a traditional PSG for sleep staging and apnea-hypopnea index.

METHODS

Simultaneous traditional PSG and patch-based PSG study data were obtained in a sleep laboratory from 206 participants with a suspected sleep disorder recruited from 7 clinical sites. Blinded, randomized scoring of the traditional PSG and patch-based PSG recordings was completed according to The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications (Version 2.6) criteria by 3 independent scorers.

RESULTS

Concordance correlation coefficients were high between the patch-based device and traditional PSG across essential sleep and respiratory variables-total sleep time (.87); wake (.84); non-rapid eye movement (REM) (.80); non-REM sleep stage 1 (N1) (.72); non-REM sleep stage 2 (N2) (.71); non-REM sleep stage 3 (N3) (.64); REM (.80); and apnea-hypopnea index (AHI) (.94). There was substantial agreement between epoch sleep staging scored on the patch-based device and traditional PSG (average Cohen's kappa of 0.62 ± 0.13 across all scorers).

CONCLUSIONS

The patch-based type-II PSG had a similar performance on sleep staging and respiratory variables when compared to traditional PSG, thus making it possible to use the patch-based PSG for a routine PSG study. These results open the possibility of performing unattended PSG studies efficiently and accurately outside the sleep laboratory improving access to high quality sleep assessments for patients with sleep disorders.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: Validation Study of a Patch-based PSG System; URL: https://clinicaltrials.gov/study/NCT05310708; Identifier: NCT05310708.

CITATION

Viniol C, Galetke W, Woehrle H, et al. Clinical validation of a wireless patch-based polysomnography system. J Clin Sleep Med. 2025;21(5):813-823.

Louis EK. Validation of automated detection of REM sleep without atonia using in-laboratory and in-home recordings

STUDY OBJECTIVES

To evaluate the concordance between visual scoring and automated detection of rapid eye movement sleep without atonia (RSWA) and the validity and reliability of in-home automated-RSWA detection in patients with rapid eye movement sleep behavior disorder (RBD) and a control group.

METHODS

Sleep Profiler signals were acquired during simultaneous in-laboratory polysomnography in 24 isolated patients with RBD. Chin and arm RSWA measures visually scored by an expert sleep technologist were compared to algorithms designed to automate RSWA detection. In a second cohort, the accuracy of automated-RSWA detection for discriminating between RBD and control group (n = 21 and 42, respectively) was assessed in multinight in-home recordings.

RESULTS

For the in-laboratory studies, agreement between visual and auto-scored RSWA from the chin and arm were excellent, with intraclass correlations of 0.89 and 0.95, respectively, and substantial, based on Kappa scores of 0.68 and 0.74, respectively. For classification of patients with iRBD vs controls, specificities derived from auto-detected RSWA densities obtained from in-home recordings were 0.88 for the chin, 0.93 for the arm, and 0.90 for the chin or arm, while the sensitivities were 0.81, 0.81, and 0.86, respectively. The night-to-night consistencies of the respective auto-detected RSWA densities were good based on intraclass correlations of 0.81, 0.79, and 0.84, however some night-to-night disagreements in abnormal RSWA detection were observed.

CONCLUSIONS

When compared to expert visual RSWA scoring, automated RSWA detection demonstrates promise for detection of RBD. The night-to-night reliability of chin- and arm-RSWA densities acquired in-home were equivalent.

CITATION

Levendowski DJ, Chahine LM, Lewis SJG, et al. Validation of automated detection of REM sleep without atonia using in-laboratory and in-home recordings. J Clin Sleep Med. 2025;21(3):583-592.

From mechanisms to future therapy: a synopsis of isolated REM sleep behavior disorder as early synuclein-related disease

Parkinson disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy are synucleinopathies, characterized by neuronal loss, gliosis and the abnormal deposition of α-synuclein in vulnerable areas of the nervous system. Neurodegeneration begins however several years before clinical onset of motor, cognitive or autonomic symptoms. The isolated form of REM sleep behavior disorder (RBD), a parasomnia with dream enactment behaviors and excessive muscle activity during REM sleep, is an early stage synucleinopathy. The neurophysiological hallmark of RBD is REM sleep without atonia (RWSA), i.e. the loss of physiological muscle atonia during REM sleep. RBD pathophysiology is not fully clarified yet, but clinical and basic science suggest that ɑ-syn pathology begins in the lower brainstem where REM atonia circuits are located, including the sublaterodorsal tegmental/subcoeruleus nucleus and the ventral medulla, then propagates rostrally to brain regions such as the substantia nigra, limbic system, cortex. Genetically, there is only a partial overlap between RBD, PD and DLB, and individuals with iRBD may represent a specific subpopulation. A genome-wide association study identified five loci, which all seem to revolve around the GBA1 pathway. iRBD patients often show subtle motor, cognitive, autonomic and/or sensory signs, neuroimaging alterations as well as biofluid and tissue markers of neurodegeneration (in particular pathologic α-synuclein aggregates), which can be useful for risk stratification. Patients with iRBD represent thus the ideal population for neuroprotective/neuromodulating trials. This review provides insights into these aspects, highlighting and substantiating the central role of iRBD in treatment development strategies for synucleinopathies.

Concordance and test-retest consistency of sleep biomarker-based neurodegenerative disorder profiling

Biomarkers that aid in early detection of neurodegeneration are needed to enable early symptomatic treatment and enable identification of people who may benefit from neuroprotective interventions. Increasing evidence suggests that sleep biomarkers may be useful, given the bi-directional relationship between sleep and neurodegeneration and the prominence of sleep disturbances and altered sleep architectural characteristics in several neurodegenerative disorders. This study aimed to demonstrate that sleep can accurately characterize specific neurodegenerative disorders (NDD). A four-class machine-learning algorithm was trained using age and nine sleep biomarkers from patients with clinically-diagnosed manifest and prodromal NDDs, including Alzheimer's disease dementia (AD = 27), Lewy body dementia (LBD = 18), and isolated REM sleep behavior disorder (iRBD = 15), as well as a control group (CG = 58). The algorithm was validated in a total of 381 recordings, which included the training data set plus an additional AD = 10, iRBD = 18, Parkinson disease without dementia (PD = 29), mild cognitive impairment (MCI = 78) and CG = 128. Test-retest consistency was then assessed in LBD = 10, AD = 9, and CG = 46. The agreement between the NDD profiles and their respective clinical diagnoses exceeded 75% for the AD, LBD, and CG, and improved when NDD participants classified Likely Normal with NDD indications consistent with their clinical diagnosis were considered. Profiles for iRBD, PD and MCI participants were consistent with the heterogeneity of disease severities, with the majority of overt disagreements explained by normal sleep characterization in 27% of iRBD, 21% of PD, and 26% of MCI participants. For test-retest assignments, the same or similar NDD profiles were obtained for 88% of LBD, 86% in AD, and 98% of CG participants. The potential utility for NDD subtyping based on sleep biomarkers demonstrates promise and requires further prospective development and validation in larger NDD cohorts.

Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review

Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.

Louis EK. Proof-of-concept for characterization of neurodegenerative disorders utilizing two non-REM sleep biomarkers

Study objective

This proof-of-concept study aimed to determine whether the combined features of two non-rapid eye movement (NREM) sleep biomarkers acquired predominantly in-home could characterize different neurodegenerative disorders.

Methods

Sleep spindle duration and non-REM hypertonia (NRH) were evaluated in seven groups including a control group (CG = 61), and participants with isolated REM sleep behavior disorder (iRBD = 19), mild cognitive impairment (MCI = 41), Parkinson disease (PD = 16), Alzheimer disease dementia (ADem = 29), dementia with Lewy Bodies or Parkinson disease dementia (LBD = 19) and progressive supranuclear palsy (PSP = 13). One-way analysis of variance (ANOVA), Mann-Whitney U, intra-class (ICC) and Spearman ranked correlations, Bland-Altman plots and Kappa scores, Chi-square and Fisher exact probability test, and multiple-logistic regression were focused primarily on spindle duration and NRH and the frequencies assigned to the four normal/abnormal spindle duration/NRH combinations.

Results

ANOVA identified group differences in age, sleep efficiency, REM, NRH (p < 0.0001) and sleep time (p = 0.015), Spindle duration and NRH each demonstrated good night-to-night reliabilities (ICC = 0.95 and 0.75, Kappa = 0.93 and 0.66, respectively) and together exhibited an association in the PD and LBD groups only (p < 0.01). Abnormal spindle duration was greater in records of PSP (85%) and LBD (84%) patients compared to CG, MCI, PD and ADem (p < 0.025). Abnormal NRH was greater in PSP = 92%, LBD = 79%, and iRBD = 74% compared to MCI = 32%, ADem = 17%, and CG = 16% (p < 0.005).The combination biomarker normal spindle duration/normal NRH was observed most frequently in CG (56%) and MCI (41%). ADem most frequently demonstrated normal spindle duration/normal NRH (45%) and abnormal spindle duration/normal NRH (38%). Normal spindle duration/abnormal NRH was greatest in iRBD = 47%, while abnormal spindle duration/abnormal NRH was predominant in PSP = 85% and LBD = 74%.

Conclusion

The NREM sleep biomarkers spindle duration and NRH may be useful in distinguishing patients with different neurodegenerative disorders. Larger prospective cohort studies are needed to determine whether spindle duration and NRH can be combined for prodromal assessment and/or monitoring disease progression.