Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Patients with obstructive sleep apnea (OSA) syndrome experience repeated periods of apnea and arousal during sleep. A condition which in short term leads to excessive daytime sleepiness and in the long term may have clinical consequences such as stroke and cardiovascular abnormalities. Although complex equipment can be used to screen for sleep apnea, the screening tests are often expensive, inconvenient for the patient, and time-consuming to be manually analysed. This research investigates methods for automating sleep apnea screening using low-cost off-body cameras. Polysomnography video recordings of twenty one patients, 11 with OSA, and 10 'normals' who were referred for suspected OSA, were analysed with the objective to differentiate the two groups. The proposed technique is based on motion estimation in videos using two successive video frames. The complexities of motion signals from the video data were analysed by calculating sample entropy over multiple time scales. The sample entropy values providing the best separation between the OSA and non-OSA groups were chosen using the Bhattacharyya distance and were then used as the input to a support vector machine classifier. The classification results both on the training and validation data indicate that patients with OSA can be differentiated from patients without OSA with 90% accuracy. © 2012 IEEE.

Original publication

DOI

10.1109/BHI.2012.6211731

Type

Journal article

Journal

Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012

Publication Date

30/07/2012

Pages

890 - 893