A method to screen obstructive sleep apnea using multi-variable non-intrusive measurements

• Published in: Physiological measurement 2011-04, Vol.32 (4), p.445-465
• Main Authors: de Silva, S, Abeyratne, U R, Hukins, C
• Format: Article
• Language: English
• Subjects: Clinical Laboratory Techniques - methods; Databases, Factual; Humans; Male; Middle Aged; Neck - anatomy & histology; Neural Networks (Computer); Probability; Reproducibility of Results; Sleep Apnea, Obstructive - diagnosis; Snoring; Sound; Time Factors
• ISSN: 0967-3334; 1361-6579
• DOI: 10.1088/0967-3334/32/4/006

Obstructive sleep apnea (OSA) is a serious sleep disorder. The current standard OSA diagnosis method is polysomnography (PSG) testing. PSG requires an overnight hospital stay while physically connected to 10-15 channels of measurement. PSG is expensive, inconvenient and requires the extensive involvement of a sleep technologist. As such, it is not suitable for community screening. OSA is a widespread disease and more than 80% of sufferers remain undiagnosed. Simplified, unattended and cheap OSA screening methods are urgently needed. Snoring is commonly associated with OSA but is not fully utilized in clinical diagnosis. Snoring contains pseudo-periodic packets of energy that produce characteristic vibrating sounds familiar to humans. In this paper, we propose a multi-feature vector that represents pitch information, formant information, a measure of periodic structure existence in snore episodes and the neck circumference of the subject to characterize OSA condition. Snore features were estimated from snore signals recorded in a sleep laboratory. The multi-feature vector was applied to a neural network for OSA/non-OSA classification and K-fold cross-validated using a random sub-sampling technique. We also propose a simple method to remove a specific class of background interference. Our method resulted in a sensitivity of 91 ± 6% and a specificity of 89 ± 5% for test data for AHI(THRESHOLD) = 15 for a database consisting of 51 subjects. This method has the potential as a non-intrusive, unattended technique to screen OSA using snore sound as the primary signal.