Akademik Veri Yönetim Sistemi
Biomedical Signal Processing and Control, cilt.84, 2023 (SCI-Expanded)
Studies on the detection of Obstructive Sleep Apnea (OSA) from speech recording have brought an important innovation to this field. This study is based on the hypothesis that the deterioration of the muscles and tissues in the vocal tract in OSA patients changes the nonlinear properties of voice. In this study, the features that reveal the nonlinear structure of the speech for OSA detection were investigated. The nonlinear characteristics were evaluated for vowels and consonants, and it was tried to find out in which voice group the nonlinear features were more distinctive in OSA. The nonlinear analysis was applied to a wide variety of voice samples having vocal tract components affected by OSA, and OSA/healthy classifications were realized. The results revealed that nonlinear analysis gives considerable findings in OSA detection, and consonants are more successful than vowels. For classifications, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) classifiers, 5-fold cross validation procedure and Minimum Redundancy Maximum Relevance (MRMR) feature selection method were used. Using the whole dataset, OSA detection performance for vowels was found as 83.5% with KNN, and 96% with SVM for consonants. Additionally, the test process was carried out by using a distinct group, and 82.5% test accuracy was achieved with only 6 features for consonants. The results indicated that the proposed study supports the hypothesis that the nonlinear behavior of vocal tract changes in subjects with OSA, especially for consonants, and has considerable potential for OSA detection as the pre-diagnosis or screening test in clinical use.