Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Obstructive sleep apnea (OSA) is a chronic and prevalent condition with well-established comorbidities. However, many severe cases remain undiagnosed due to poor access to polysomnography (PSG), the gold standard for OSA diagnosis. Accurate home-based methods to screen for OSA are needed, which can be applied inexpensively to high-risk subjects to identify those that require PSG to fully assess their condition. A number of methods that analyse speech or breathing sounds to screen for OSA have been previously investigated. However, these methods have constraints that limit their use in home environments (e.g., they require specialised equipment, are not robust to background noise, are obtrusive or depend on tightly controlled conditions). This paper proposes a novel method to screen for OSA, which analyses sleep breathing sounds recorded with a smartphone at home. Audio recordings made over a whole night are divided into segments, each of which is classified for the presence or absence of OSA by a deep neural network. The percentage of segments predicted as containing evidence of OSA is then used to screen for the condition. Audio recordings made during home sleep apnea testing from 103 participants for 1 or 2 nights were used to develop and evaluate the proposed system. When screening for moderate OSA the acoustics based system achieved a sensitivity of 0.79 and a specificity of 0.80. The sensitivity and specificity when screening for severe OSA were 0.78 and 0.93, respectively. The system is suitable for implementation on consumer smartphones.

Original publication




Journal article


IEEE Journal of Biomedical and Health Informatics

Publication Date