Bayes classification of snoring subjects with and without Sleep Apnea Hypopnea Syndrome, using a Kernel method

The gold standard for diagnosing Sleep Apnea Hypopnea Syndrome (SAHS) is the Polysomnography (PSG), an expensive, labor-intensive and time-consuming procedure. It would be helpful to have a simple screening method that allowed to early determining the severity of a subject prior to his/her enrolment for a PSG. Several differences have been reported in the acoustic snoring characteristics between simple snorers and SAHS patients. Previous studies usually classify snoring subjects into two groups given a threshold of Apnea-Hypoapnea Index (AHI). Recently, Bayes multi-group classification with Gaussian Probability Density Function (PDF) has been proposed, using snore features in combination with apnea-related information. In this work we show that the Bayes classifier with Kernel PDF estimation outperforms the Gaussian approach and allows the classification of SAHS subjects according to their severity, using only the information obtained from snores. This could be the base of a single channel, snore-based, screening procedure for SAHS.

[1]  Raimon Jané,et al.  Continuous analysis and monitoring of snores and their relationship to the apnea‐hypopnea index , 2010, The Laryngoscope.

[2]  R. Jané,et al.  Pitch analysis in snoring signals from simple snorers and patients with obstructive sleep apnea , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[3]  J.A. Fiz,et al.  Variability of snore parameters in time and frequency domains in snoring subjects with and without Obstructive Sleep Apnea , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[4]  Atul Malhotra,et al.  Pathophysiology of adult obstructive sleep apnea. , 2008, Proceedings of the American Thoracic Society.

[5]  Y. Zigel,et al.  Nocturnal sound analysis for the diagnosis of obstructive sleep apnea , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[6]  Ronald M. Aarts,et al.  The acoustics of snoring. , 2010, Sleep medicine reviews.

[7]  W. Flemons,et al.  Home diagnosis of sleep apnea: a systematic review of the literature. An evidence review cosponsored by the American Academy of Sleep Medicine, the American College of Chest Physicians, and the American Thoracic Society. , 2003, Chest.

[8]  U. Abeyratne,et al.  Multi-feature snore sound analysis in obstructive sleep apnea-hypopnea syndrome. , 2011, Physiological measurement.

[9]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  U. Abeyratne,et al.  Could formant frequencies of snore signals be an alternative means for the diagnosis of obstructive sleep apnea? , 2007, Sleep medicine.

[11]  J.A. Fiz,et al.  Spectral envelope analysis in snoring signals from simple snorers and patients with Obstructive Sleep Apnea , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[12]  R. Jane,et al.  Automatic snoring signal analysis in sleep studies , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).