Performance Evaluation of the Circadia Contactless Respiratory Monitor and Circadia Sleep Analysis Algorithm

Abstract—The current study aims to evaluate the performance of the Circadia Sleep Analysis Algorithm, which - in combination with the non-contact and minimally invasive Circadia bedside monitor - allows for long-term sleep assessment and continuous evaluation of sleep therapy outcomes. The Circadia Contactless Respiratory Monitor was initially used to record 17 nights of sleep data from 9 participants alongside polysomnography (PSG), with a subsequent 24 nights of PSG data for validation purposes. Vital sign and body movement features were extracted from sensor data, and a machine learning algorithm was developed to perform sleep stage prediction. The algorithm was trained using PSG data, and validated by leave-one-subject-out cross validation. An epoch-by-epoch recall (true positive rate) of 75.0%, 59.9%, 74.8% and 57.1%, was found for Deep, Light, REM and Wake respectively, in the initial 17 night dataset. Highly similar results were obtained in the independent validation dataset of 24 nights, indicating robustness of results and generalisability of the sleep staging algorithm. The Circadia device and Sleep Analysis Algorithm were found to outperform both consumer and medical grade wrist-worn actimetry devices (Fitbit Alta HR and Philips Respironics Actiwatch) on sleep metric estimation accuracy. In a comparison of published sleep staging performance against alternative non-contact consumer sleep tracking devices, the Circadia device achieves highest overall performance. 

These results show that contactless sleep tracking using the developed monitor is highly accurate, and PSG accuracy is being approached (considering a PSG inter-rater-agreement rate of 82%). This suggests that the developed non-contact monitor forms a viable alternative to existing clinically used wrist-worn methods, and that longitudinal monitoring of sleep stages in a home environment becomes feasible.