Diagnosis of Obstructive Sleep Apnea Using Speech Signals From Awake Subjects

Obstructive sleep apnea (OSA) is a sleep disorder in which pharyngeal collapse during sleep causes complete (apnea) or partial (hypopnea) airway obstruction. OSA is common and can have severe implications, but often remains undiagnosed. The most widely used objective measure of OSA severity is the number of obstructive events per hour of sleep, known as the apnea-hypopnea index (AHI). This study reports an innovative system to identify OSA subjects while they are awake, not asleep, using speech signal processing techniques. The assumption is that OSA affects the acoustic parameters of speech because it is associated with anatomical and functional abnormalities of the upper airway. The system associates three different sub-systems based on features extracted from breathing segments within continuous speech signals, information acquired from sustained vowels using a convolutional neural network, and inherent information in continuous speech signals using a recurrent neural network. Each of these sub-systems provided an AHI estimation and were combined with age and body mass index (BMI) to produce a composite system that estimates AHI using a linear regression. The sample was composed of 398 subjects (men and women). The performance of each sub-system was examined separately, in addition to the composite system. As expected, the composite AHI estimation yielded the superior results, with a Pearson correlation coefficient of 0.61 between the estimated and diagnosed AHI. To distinguish between OSA and non-OSA subjects, a classification decision was made using an AHI threshold of 15 events per hour. The system achieved an average accuracy of 77.14%, a sensitivity of 75%, and a specificity of 79%.