Sleep Apnea Detection via Depth Video and Audio Feature Learning

Obstructive sleep apnea, characterized by repetitive obstruction in the upper airway during sleep, is a common sleep disorder that could significantly compromise sleep quality and quality of life in general. The obstructive respiratory events can be detected by attended in-laboratory or unattended ambulatory sleep studies. Such studies require many attachments to a patient's body to track respiratory and physiological changes, which can be uncomfortable and compromise the patient's sleep quality. In this paper, we propose to record depth video and audio of a patient using a Microsoft Kinect camera during his/her sleep, and extract relevant features to correlate with obstructive respiratory events scored manually by a scientific officer based on data collected by Philips system Alice6 LDxS that is commonly used in sleep clinics. Specifically, we first propose an alternating-frame H.264 video encoding scheme and bit recovery scheme at the decoder. Next, we perform depth video temporal denoising using a motion vector graph smoothness prior. Then, we build a dual-ellipse model and track a patient's chest and abdominal movements in the denoised videos. Finally, we extract features from both depth video and audio for classifier training and respiratory event detection. Experimental results show 1) that our depth video compression scheme outperforms a competitor that records only the 8 most significant bits, 2) our graph-based temporal denoising scheme reduces the flickering effect without over-smoothing, and 3) our trained classifiers can deduce respiratory events scored manually based on data collected by system Alice6 LDxS with high accuracy.