Contactless Camera-Based Sleep Staging: The HealthBed Study.

Polysomnography (PSG) remains the gold standard for sleep monitoring but is obtrusive in nature. Advances in camera sensor technology and data analysis techniques enable contactless monitoring of heart rate variability (HRV). In turn, this may allow remote assessment of sleep stages, as different HRV metrics indirectly reflect the expression of sleep stages. We evaluated a camera-based remote photoplethysmography (PPG) setup to perform automated classification of sleep stages in near darkness. Based on the contactless measurement of pulse rate variability, we use a previously developed HRV-based algorithm for 3 and 4-class sleep stage classification. Performance was evaluated on data of 46 healthy participants obtained from simultaneous overnight recording of PSG and camera-based remote PPG. To validate the results and for benchmarking purposes, the same algorithm was used to classify sleep stages based on the corresponding ECG data. Compared to manually scored PSG, the remote PPG-based algorithm achieved moderate agreement on both 3 class (Wake-N1/N2/N3-REM) and 4 class (Wake-N1/N2-N3-REM) classification, with average κ of 0.58 and 0.49 and accuracy of 81% and 68%, respectively. This is in range with other performance metrics reported on sensing technologies for wearable sleep staging, showing the potential of video-based non-contact sleep staging.

Modeling sleep onset misperception in insomnia.

OBJECTIVES: To extend and validate a previously suggested model of the influence of uninterrupted sleep bouts on sleep onset misperception in a large independent data set. METHODS: Polysomnograms and sleep diaries of 139 insomnia patients and 92 controls were included. We modeled subjective sleep onset as the start of the first uninterrupted sleep fragment longer than Ls minutes, where parameter Ls reflects the minimum length of a sleep fragment required to be perceived as sleep. We compared the so-defined sleep onset latency (SOL) for various values of Ls. Model parameters were compared between groups, and across insomnia subgroups with respect to sleep onset misperception, medication use, age, and sex. Next, we extended the model to incorporate the length of wake fragments. Model performance was assessed by calculating root mean square errors (RMSEs) of the difference between estimated and perceived SOL. RESULTS: Participants with insomnia needed a median of 34 minutes of undisturbed sleep to perceive sleep onset, while healthy controls needed 22 minutes (Mann-Whitney U = 4426, p < 0.001). Similar statistically significant differences were found between sleep onset misperceivers and non-misperceivers (median 40 vs. 20 minutes, Mann-Whitney U = 984.5, p < 0.001). Model outcomes were similar across other subgroups. Extended models including wake bout lengths resulted in only marginal improvements of model outcome. CONCLUSIONS: Patients with insomnia, particularly sleep misperceivers, need larger continuous sleep bouts to perceive sleep onset. The modeling approach yields a parameter for which we coin the term Sleep Fragment Perception Index, providing a useful measure to further characterize sleep state misperception.

Application of a Limit-Cycle Oscillator Model for Prediction of Circadian Phase in Rotating Night Shift Workers.

Practical alternatives to gold-standard measures of circadian timing in shift workers are needed. We assessed the feasibility of applying a limit-cycle oscillator model of the human circadian pacemaker to estimate circadian phase in 25 nursing and medical staff in a field setting during a transition from day/evening shifts (diurnal schedule) to 3-5 consecutive night shifts (night schedule). Ambulatory measurements of light and activity recorded with wrist actigraphs were used as inputs into the model. Model estimations were compared to urinary 6-sulphatoxymelatonin (aMT6s) acrophase measured on the diurnal schedule and last consecutive night shift. The model predicted aMT6s acrophase with an absolute mean error of 0.69 h on the diurnal schedule (SD = 0.94 h, 80% within ±1 hour), and 0.95 h on the night schedule (SD = 1.24 h, 68% within ±1 hour). The aMT6s phase shift from diurnal to night schedule was predicted to within ±1 hour in 56% of individuals. Our findings indicate the model can be generalized to a shift work setting, although prediction of inter-individual variability in circadian phase shift during night shifts was limited. This study provides the basis for further adaptation and validation of models for predicting circadian phase in rotating shift workers.

Protocol of the SOMNIA project: an observational study to create a neurophysiological database for advanced clinical sleep monitoring.

INTRODUCTION: Polysomnography (PSG) is the primary tool for sleep monitoring and the diagnosis of sleep disorders. Recent advances in signal analysis make it possible to reveal more information from this rich data source. Furthermore, many innovative sleep monitoring techniques are being developed that are less obtrusive, easier to use over long time periods and in the home situation. Here, we describe the methods of the Sleep and Obstructive Sleep Apnoea Monitoring with Non-Invasive Applications (SOMNIA) project, yielding a database combining clinical PSG with advanced unobtrusive sleep monitoring modalities in a large cohort of patients with various sleep disorders. The SOMNIA database will facilitate the validation and assessment of the diagnostic value of the new techniques, as well as the development of additional indices and biomarkers derived from new and/or traditional sleep monitoring methods. METHODS AND ANALYSIS: We aim to include at least 2100 subjects (both adults and children) with a variety of sleep disorders who undergo a PSG as part of standard clinical care in a dedicated sleep centre. Full-video PSG will be performed according to the standards of the American Academy of Sleep Medicine. Each recording will be supplemented with one or more new monitoring systems, including wrist-worn photoplethysmography and actigraphy, pressure sensing mattresses, multimicrophone recording of respiratory sounds including snoring, suprasternal pressure monitoring and multielectrode electromyography of the diaphragm. ETHICS AND DISSEMINATION: The study was reviewed by the medical ethical committee of the Maxima Medical Center (Eindhoven, the Netherlands, File no: N16.074). All subjects provide informed consent before participation.The SOMNIA database is built to facilitate future research in sleep medicine. Data from the completed SOMNIA database will be made available for collaboration with researchers outside the institute.