Detection of severe obstructive sleep apnea through voice analysis
The present study focuses on voice alone as a primary discriminating source of information between healthy subjects and severe OSA. Statistical analysis as well as performance of several classifiers indicate that voice has a clear potential to detect severe OSA. Analysing the features we conclude that vowel and phrase features and both uttering positions are useful. CCR, Sensitivity and Specificity (all above 80%) point out the potential of voice as a discriminating factor between healthy subjects and severe OSA Future work will focus on clinical validation results for a comprehensive body of new subjects, with an already trained classifier using this model.
obstructive sleep apnea voice processing genetic algorithms feature reduction