Improving Automatic Detection of Obstructive Sleep Apnea Through Nonlinear Analysis of Sustained Speech
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We present a novel approach for the detection of severe obstructive sleep apnea (OSA) based on patients’ voices introducing nonlinear measures to describe sustained speech dynamics. Nonlinear features were combined with state-of-the-art speech recognition systems using statistical modeling techniques (Gaussian mixture models, GMMs) over cepstral parameterization (MFCC) for both continuous and sustained speech. Tests were performed on a database including speech records from both severe OSA and control speakers. A 10 % relative reduction in classification error was obtained for sustained speech when combining MFCC-GMM and nonlinear features, and 33 % when fusing nonlinear features with both sustained and continuous MFCC-GMM. Accuracy reached 88.5 % allowing the system to be used in OSA early detection. Tests showed that nonlinear features and MFCCs are lightly correlated on sustained speech, but uncorrelated on continuous speech. Results also suggest the existence of nonlinear effects in OSA patients’ voices, which should be found in continuous speech.
KeywordsObstructive sleep apnea (OSA) Continuous speech Sustained speech Gaussian mixture models (GMMs) Nonlinear analysis Speech dynamics Classification and regression tree (CART)
The activities described in this paper were funded by the Spanish Ministry of Science and Innovation as part of the TEC2009-14719-C02-02 (PriorSpeech) project. The corresponding author also acknowledges the support from Universidad Politécnica de Madrid full-time PhD scholarship program. Finally, authors would like to thank Athanasios Tsanas, Max Little and Professor J. I. Godino Llorente, for their comments and suggestions.
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