Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera
This work addresses the need for non-invasive sleep monitoring for patients, offering a more comfortable alternative to contact sensors, though it is incremental as it builds on existing video-based methods.
The paper tackled the problem of uncomfortable and expensive conventional sleep monitoring by developing a method for sleep stage classification using heart rate, breathing rate, and activity measures derived from a near-infrared video camera, achieving an accuracy of 73.4% and a Cohen's kappa of 0.61 in four-class classification.
Conventional sleep monitoring is time-consuming, expensive and uncomfortable, requiring a large number of contact sensors to be attached to the patient. Video data is commonly recorded as part of a sleep laboratory assessment. If accurate sleep staging could be achieved solely from video, this would overcome many of the problems of traditional methods. In this work we use heart rate, breathing rate and activity measures, all derived from a near-infrared video camera, to perform sleep stage classification. We use a deep transfer learning approach to overcome data scarcity, by using an existing contact-sensor dataset to learn effective representations from the heart and breathing rate time series. Using a dataset of 50 healthy volunteers, we achieve an accuracy of 73.4\% and a Cohen's kappa of 0.61 in four-class sleep stage classification, establishing a new state-of-the-art for video-based sleep staging.