Study Objectives To validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected sleep disordered patients. Methods We applied a previously designed algorithm for automatic sleep staging using long short-term memory recurrent neural networks to model sleep architecture. The classifier uses 132 HRV features computed from electrocardiography and activity counts from accelerometry. We retrained our algorithm using two public datasets containing both healthy sleepers and sleep disordered patients. We then tested the performance of the algorithm on an independent hold-out validation set of sleep recordings from a wide range of sleep disorders collected in a tertiary sleep medicine center. Results The classifier achieved substantial agreement on four-class sleep staging (wake/N1–N2/N3/rapid eye movement [REM]), with an average κ of 0.60 and accuracy of 75.9%. The performance of the sleep staging algorithm was significantly higher in insomnia patients (κ = 0.62, accuracy = 77.3%). Only in REM parasomnias, the performance was significantly lower (κ = 0.47, accuracy = 70.5%). For two-class wake/sleep classification, the classifier achieved a κ of 0.65, with a sensitivity (to wake) of 72.9% and specificity of 94.0%. Conclusions This study shows that the combination of HRV, body movements, and a state-of-the-art deep neural network can reach substantial agreement in automatic sleep staging compared with polysomnography, even in patients suffering from a multitude of sleep disorders. The physiological signals required can be obtained in various ways, including non-obtrusive wrist-worn sensors, opening up new avenues for clinical diagnostics.

A large part of the worldwide population suffers from obstructive sleep apnea (OSA), a disorder impairing the restorative function of sleep and constituting a risk factor for several cardiovascular pathologies. The standard diagnostic metric to define OSA is the apnea–hypopnea index (AHI), typically obtained by manually annotating polysomnographic recordings. However, this clinical procedure cannot be employed for screening and for long-term monitoring of OSA due to its obtrusiveness and cost. Here, we propose an automatic unobtrusive AHI estimation method fully based on wrist-worn reflective photoplethysmography (rPPG), employing a deep learning model exploiting cardiorespiratory and sleep information extracted from the rPPG signal trained with 250 recordings. We tested our method with an independent set of 188 heterogeneously disordered clinical recordings and we found it estimates the AHI with a good agreement to the gold standard polysomnography reference (correlation = 0.61, estimation error = 3±10 events/h). The estimated AHI was shown to reliably assess OSA severity (weighted Cohen’s kappa = 0.51) and screen for OSA (ROC–AUC = 0.84/0.86/0.85 for mild/moderate/severe OSA). These findings suggest that wrist-worn rPPG measurements that can be implemented in wearables such as smartwatches, have the potential to complement standard OSA diagnostic techniques by allowing unobtrusive sleep and respiratory monitoring.

Background Parkinson’s disease is a complex neurological disorder characterized by a variety of motor- as well as non-motor symptoms. Video-based technology (using continuous home monitoring) may bridge the gap between the fragmented in-clinic observations and the need for a comprehensive understanding of the progression and fluctuation of disease symptoms. However, continuous monitoring can be intrusive, raising questions about feasibility as well as potential privacy violation. Methods We used a grounded theory approach in which we performed semi-structured interviews to explore the opinion of Parkinson’s patients on home-based video recording used for vision-based movement analysis. Results Saturation was reached after sixteen interviews. Three first–level themes were identified that specify the conditions required to perform continuous video monitoring: Camera recording (e.g. being able to turn off the camera), privacy protection (e.g. patient’s behaviour, patient’s consent, camera location) and perceived motivation (e.g. contributing to science or clinical practice). Conclusion Our findings show that Parkinson patients’ perception of continuous, home-based video recording is positive, when a number of requirements are taken into account. This knowledge will enable us to start using this technology in future research and clinical practice in order to better understand the disease and to objectify outcomes in the patients’ own homes.