title: Automatic detection of obstructive sleep apnea using speech signals.
abstract: Obstructive sleep apnea (OSA) is a common disorder associated with anatomical abnormalities of the upper airways that affects 5% of the population. Acoustic parameters may be influenced by the vocal tract structure and soft tissue properties. We hypothesize that speech signal properties of OSA patients will be different than those of control subjects not having OSA. Using speech signal processing techniques, we explored acoustic speech features of 93 subjects who were recorded using a text-dependent speech protocol and a digital audio recorder immediately prior to polysomnography study. Following analysis of the study, subjects were divided into OSA (n=67) and non-OSA (n=26) groups. A Gaussian mixture model-based system was developed to model and classify between the groups; discriminative features such as vocal tract length and linear prediction coefficients were selected using feature selection technique. Specificity and sensitivity of 83% and 79% were achieved for the male OSA and 86% and 84% for the female OSA patients, respectively. We conclude that acoustic features from speech signals during wakefulness can detect OSA patients with good specificity and sensitivity. Such a system can be used as a basis for future development of a tool for OSA screening.
authors.affiliation: BlueLibris, Inc., Menlo Park, CA 94026, USA. evgeniag@bgu.ac.il
authors.forename: Evgenia
authors.initials: E
authors.lastname: Goldshtein
authors.forename: Ariel
authors.initials: A
authors.lastname: Tarasiuk
authors.forename: Yaniv
authors.initials: Y
authors.lastname: Zigel
mesh_terms: D000328:Adult; D000368:Aged; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016011:Normal Distribution; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000503:physiopathology; D013018:Sound Spectrography / Q000379:methods*; D013061:Speech Acoustics; D014851:Wakefulness