GMM-based Classifiers for the Automatic Detection of Obstructive Sleep Apnea

The aim of automatic pathological voice detection systems is to serve as tools, to medical specialists, for a more objective, less invasive and improved diagnosis of diseases. In this respect, the gold standard for those system include the usage of a optimized representation of the spectral envelope, either based on cepstral coefficients from the mel-scaled Fourier spectral envelope (Mel-Frequency Cepstral Coefficients) or from an all-pole estimation (Linear Prediction Coding Cepstral Coefficients) forcharacterization, and Gaussian Mixture Models for posterior classification. However, the study of recently proposed GMM-based classifiers as well as Nuisance mitigation techniques, such as those employed in speaker recognition, has not been widely considered inpathology detection labours. The present work aims at testing whether or not the employment of such speaker recognition tools might contribute to improve system performance in pathology detection systems, specifically in the automatic detection of Obstructive Sleep Apnea. The testing procedure employs an Obstructive Sleep Apnea database, in conjunction with GMM-based classifiers looking for a better performance. The results show that an improved performance might be obtained by using such approach.

[1]  Federica Provini,et al.  Cardiovascular Disorders and Obstructive Sleep Apnea Syndrome , 2006, Clinical and experimental hypertension.

[2]  P. Lloberes,et al.  Self-reported sleepiness while driving as a risk factor for traffic accidents in patients with obstructive sleep apnoea syndrome and in non-apnoeic snorers. , 2000, Respiratory medicine.

[3]  Driss Matrouf,et al.  State-of-the-Art Performance in Text-Independent Speaker Verification Through Open-Source Software , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[4]  P. Monoson,et al.  Speech dysfunction of obstructive sleep apnea. A discriminant analysis of its descriptors. , 1989, Chest.

[5]  Luis A. Hernández Gómez,et al.  Design of a Multimodal Database for Research on Automatic Detection of Severe Apnoea Cases , 2008, LREC.

[6]  William M. Campbell,et al.  Support vector machines for speaker and language recognition , 2006, Comput. Speech Lang..

[7]  Luis A. Hernández Gómez,et al.  Introducing Non-linear Analysis into Sustained Speech Characterization to Improve Sleep Apnea Detection , 2011, NOLISP.

[8]  Yonghong Yan,et al.  Discrimination between pathological and normal voices using GMM-SVM approach. , 2011, Journal of voice : official journal of the Voice Foundation.

[9]  Thomas S. Huang,et al.  Image processing , 1971 .

[10]  Yaniv Zigel,et al.  Detection of obstructive sleep apnea using speech signal analysis , 2011, MAVEBA.

[11]  A. Murray,et al.  Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings , 2002, Medical and Biological Engineering and Computing.

[12]  Eduardo López,et al.  Analyzing Training Dependencies and Posterior Fusion in Discriminant Classification of Apnea Patients Based on Sustained and Connected Speech , 2011, INTERSPEECH.

[13]  Doroteo Torre Toledano,et al.  Automatic Speaker Recognition Techniques: A New Tool for Sleep Apnoea Diagnosis. , 2009, ATS 2009.

[14]  Douglas A. Reynolds.  Gaussian Mixture Models , 2009, Encyclopedia of Biometrics.

[15]  Haizhou Li,et al.  An overview of text-independent speaker recognition: From features to supervectors , 2010, Speech Commun..

[16]  Luis A. Hernández Gómez,et al.  Analyzing GMMs to characterize resonance anomalies in speakers suffering from apnoea , 2009, INTERSPEECH.

[17]  María Victoria Rodellar Biarge,et al.  Glottal Source biometrical signature for voice pathology detection , 2009, Speech Commun..

[18]  Luis A. Hernández Gómez,et al.  Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques , 2009, EURASIP J. Adv. Signal Process..