Evaluation of a Contactless Sleep Monitoring Device for Sleep Stage Detection against Home Polysomnography in a Healthy Population
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Background
Sleep is essential for overall health and well-being, but assessing sleep architecture and quality is often costly and time-consuming, relying primarily on polysomnography (PSG). Wearable and nearable devices offer potential alternatives, but they regularly lack rigorous validation, especially in real-world settings.
Objectives
This study evaluates the accuracy and reliability of Withings Sleep Analyzer (WSA), a contactless sleep monitoring device, compared to PSG in a home setting using a large and diverse cohort of healthy individuals.
Methods
A total of 117 participants (69 women; 39.9 ± 11.4 years, mean ± std) underwent home-based polysomnography (PSG) and simultaneous WSA recording. Data analysis focused on evaluating sleep-wake distinction and sleep stage identification using standard classification metrics.
Results
WSA demonstrates high sensitivity (93%) for sleep detection and moderate sensitivity (73%) for wakefulness, achieving an overall accuracy of 87% for sleep-wake distinction. The device showed consistent performance across various demographic subgroups, including different age, BMI, mattress and sleep arrangements (with or without bed partner) categories. Challenges were noted in accurately classifying specific sleep stages, particularly in distinguishing between light and deep sleep, with a mean accuracy of 63% and a Cohen’s Kappa of 0.49. The WSA tended to overestimate total sleep time (+20 min) and light sleep (+1h21 min) while underestimating REM (−15 min) and deep sleep (−46 min) durations. Disagreements between expert reviewers, particularly between light and deep sleep stages, mirrored in part the WSA’s misclassifications. Participants reported significantly altered perceived sleep quality during the night with the PSG, suggesting potential discomfort during sleep.
Conclusions
WSA offers a promising approach to sleep monitoring in natural home environments. Being contactless and placed under the mattress, the WSA’s allows for long-term monitoring of sleep measures. It shows competitive performance in sleep-wake and sleep stage identification compared to other consumer devices. Progress in wearable and nearable devices is necessary to enhance their accuracy to better support the monitoring of populations with strongly impaired sleep, although limited by an imperfect gold standard. This work also emphasizes the importance of using large, diverse, and challenging datasets, as well as the need for a standardized methodology for accurate sleep stage classification.
