• Andrew Keong Ng, Tong San Koh, Eugene Baey, Teck Hock Lee, Udantha Ranjith Abeyratne, and Kathiravelu Puvanendran.
• School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Republic of Singapore. ngke0002@ntu.edu.sg
• Sleep Med. 2008 Dec 1; 9 (8): 894-8.

ObjectiveTo study the feasibility of using acoustic signatures in snore signals for the diagnosis of obstructive sleep apnea (OSA).MethodsSnoring sounds of 30 apneic snorers (24 males; 6 females; apnea-hypopnea index, AHI=46.9+/-25.7events/h) and 10 benign snorers (6 males; 4 females; AHI=4.6+/-3.4events/h) were captured in a sleep laboratory. The recorded snore signals were preprocessed to remove noise, and subsequently, modeled using a linear predictive coding (LPC) technique. Formant frequencies (F1, F2, and F3) were extracted from the LPC spectrum for analysis. The accuracy of this approach was assessed using receiver operating characteristic curves and notched box plots. The relationship between AHI and F1 was further explored via regression analysis.ResultsQuantitative differences in formant frequencies between apneic and benign snores are found in same- or both-gender snorers. Apneic snores exhibit higher formant frequencies than benign snores, especially F1, which can be related to the pathology of OSA. This study yields a sensitivity of 88%, a specificity of 82%, and a threshold value of F1=470Hz that best differentiate apneic snorers from benign snorers (both gender combined).ConclusionAcoustic signatures in snore signals carry information for OSA diagnosis, and snore-based analysis might potentially be a non-invasive and inexpensive diagnostic approach for mass screening of OSA.

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