Obstructive sleep apnea severity estimation: Fusion of speech-based systems

Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder. Previous studies associated OSA with anatomical abnormalities of the upper respiratory tract that may be reflected in the acoustic characteristics of speech. We tested the hypothesis that the speech signal carries essential information that can assist in early assessment of OSA severity by estimating apnea-hypopnea index (AHI). 198 men referred to routine polysomnography (PSG) were recorded shortly prior to sleep onset while reading a one-minute speech protocol. The different parts of the speech recordings, i.e., sustained vowels, short-time frames of fluent speech, and the speech recording as a whole, underwent separate analyses, using sustained vowels features, short-term features, and long-term features, respectively. Applying support vector regression and regression trees, these features were used in order to estimate AHI. The fusion of the outputs of the three subsystems resulted in a diagnostic agreement of 67.3% between the speech-estimated AHI and the PSG-determined AHI, and an absolute error rate of 10.8 events/hr. Speech signal analysis may assist in the estimation of AHI, thus allowing the development of a noninvasive tool for OSA screening.