Open AccessObstructive Sleep Apnea Detection Using Speech Signals with High Frequency Components
Author(s) -
Kang-Gao Pang,
AUTHOR_ID,
Tai-Chiu Hsung,
Guozhao Liao,
WingKuen Ling,
Alex Ka-Wing Law,
Wonyoung Choi
Publication year - 2022
Publication title -
journal of communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.185
H-Index - 35
eISSN - 2374-4367
pISSN - 1796-2021
DOI - 10.12720/jcm.17.1.49-55
Subject(s) - formant , speech recognition , computer science , feature selection , quadratic classifier , principal component analysis , linear discriminant analysis , pattern recognition (psychology) , speech processing , mel frequency cepstrum , feature extraction , artificial intelligence , feature (linguistics) , obstructive sleep apnea , classifier (uml) , medicine , vowel , linguistics , philosophy
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).