Abstract—In this study, the Obstructive Sleep Apnea (OSA) detection using speech signals during awake is considered. Traditional speech based OSA detection methods adopt traditional features (Formants, MFCC, etc.) on normal speech frequency range (<6kHz). However, it ignores the signal components outside this range that usually appear in pathological voices. In this paper, higher order traditional speech features (with more high frequency components) are adopted for detection. To better characterize OSA patients’ speech, a high frequency feature set is proposed. It consists of the traditional speech features with optimized parameters and a new proposed feature: High frequency energy. Principal Component Analysis (PCA) based Sequence Forward Feature Selection (PCASFFS) are adopted as feature selection. In the simulation using 66 OSA patients’ speech signals, it achieves an accuracy of 84.85% for multi-class (4 levels) detection with the proposed high frequency feature set using quadratic discriminant analysis classifier (QDA).
 

Index Terms—Obstructive sleep apnea, speech analysis, High Frequency Energy (HFE), machine learning

Cite: Kang-Gao Pang, Tai-Chiu Hsung, Guozhao Liao, Wing-Kuen Ling, Alex Ka-Wing Law, and Wing Shan Choi, "Obstructive Sleep Apnea Detection Using Speech Signals with High Frequency Components," Journal of Communications vol. 17, no. 1, pp. 49-55, January 2022. Doi: 10.12720/jcm.17.1.49-55