Performance Evaluation of the Circadia Contactless Breathing Monitor and Sleep Analysis Algorithm for Sleep Stage Classification

• Main Authors: Lauteslager, Timo, Kampakis, Stylianos, Williams, Adrian J., Maslik, Michal, Siddiqui, Fares
• Format: Conference Proceeding
• Language: English
• ISSN: 1558-4615; 2694-0604
• DOI: 10.1109/EMBC44109.2020.9175419

Although polysomnography (PSG) remains the gold standard for studying sleep in the lab, the development of wearable and 'nearable' non-EEG based sleep monitors has the potential to make long-term sleep monitoring in a home environment possible. However, validation of these novel technologies against PSG is required. The current study aims to evaluate the sleep staging performance of the radar-based Circadia Contactless Breathing Monitor (model C100) and proprietary Sleep Analysis Algorithm, both in a home and sleep lab environment, on cohorts of healthy sleepers. The C100 device was initially used to record 17 nights of sleep data from 9 participants alongside PSG, with a subsequent 24 nights of PSG data for validation purposes. Respiration 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 obtained in the initial dataset (n=17), and validated using 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. Highly similar results were obtained in the independent validation dataset (n=24), indicating robustness of results and generalizability of the sleep staging model, at least in the healthy population. The device was found to outperform both a consumer and medical grade wrist-worn monitoring device (Fitbit Alta HR and Philips Respironics Actiwatch) on sleep metric estimation accuracy. These results indicate 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.