A snoring detector for OSAHS based on patient's individual personality

A conventional diagnostic tool for assessing Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is polysomnography (PSG), which is expensive and uncomfortable for patients. It is an important and urgent topic to find a non-invasive and low-cost diagnostic approach for OSAHS detection. Recently, the snore signal analysis receives much attention due to its potential capability for OSAHS detection. In this paper, we propose a novel method for diagnosing OSAHS based on patient's individual personality. First, the first formant frequencies of each snorer are classified into two clusters by K-means clustering. And then, using the first cluster center of each snorer, we set a personalized threshold to distinguish the hypopneic snores from the normal ones. Since the proposed threshold varies with each individual, the patient's individual personality can be overcome effectively. Experimental results show the validity of the proposed detector. In the experiments, the sensitivity of our method can achieve 90% and the specificity can achieve 91.67%.

[1]  K. Puvanendran,et al.  From snoring to sleep apnea in a Singapore population. , 1999, Sleep research online : SRO.

[2]  John H. L. Hansen,et al.  Discrete-Time Processing of Speech Signals , 1993 .

[3]  Osman Erogul,et al.  Spectral envelope analysis of snoring signals , 2008 .

[4]  T. Young,et al.  Estimation of the clinically diagnosed proportion of sleep apnea syndrome in middle-aged men and women. , 1997, Sleep.

[5]  Azadeh Yadollahi,et al.  Automatic breath and snore sounds classification from tracheal and ambient sounds recordings. , 2010, Medical engineering & physics.

[6]  Yinhong Zhang,et al.  The researches of wavelet transform for the sleep apnea syndrome through snoring analysis , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

[7]  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.

[8]  R. Jané,et al.  Acoustic analysis of snoring sound in patients with simple snoring and obstructive sleep apnoea. , 1996, The European respiratory journal.

[9]  Yeh-Liang Hsu,et al.  Development of a portable device for home monitoring of snoring , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[10]  Azadeh Yadollahi,et al.  Formant analysis of breath and snore sounds , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  K. Puvanendran,et al.  Speech-like Analysis of Snore Signals for the Detection of Obstructive Sleep Apnea , 2006, 2006 International Conference on Biomedical and Pharmaceutical Engineering.

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

[13]  R. Jane,et al.  Regular and non regular snore features as markers of SAHS , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[14]  T. Young,et al.  The occurrence of sleep-disordered breathing among middle-aged adults. , 1993, The New England journal of medicine.