Obstructive Sleep Apnea Screening and Airway Structure Characterization During Wakefulness Using Tracheal Breathing Sounds

Abstract

Screening for obstructive sleep apnea (OSA) disorder during wakefulness is challenging. In this paper, we present a set of tracheal breathing sounds characteristics with classification power for separating individuals with apnea/hypopnea index (AHI)≥10 (OSA group) from those with AHI≤5 (non-OSA group) during wakefulness. Tracheal breathing sound signals were recorded during wakefulness in supine position; subjects were instructed to have a few deep breaths through their nose, then through their mouth. Study participants were 147 individuals (80 males) referred to overnight polysomnography (PSG) assessment; their AHI scores were collected after their overnight-PSG study was completed. The signals were normalized; then, their power spectra were estimated. After conducting a multi-stage process for feature extraction and selection on a subset of training data, two spectral features showing significant differences between the two groups were selected for classification. These features showed a correlation of 0.42 with AHI. A 2-class support vector machine classifier with a linear kernel was used. Following this an exhaustive leave-two-out cross-validation was performed. The overall accuracies were 83.83 and 83.92% for training and testing datasets, respectively, while the overall sensitivity and specificity of the test datasets were 82.61 and 85.22%, respectively. We also applied the same method for anthropometric information (i.e., age, weight, etc.) as features, and they resulted in an overall accuracy of 77.6 and 76.2% for training and testing datasets, respectively. The results of this study show a superior classification power of respiratory sound features compared to anthropometric features for a quick screening of OSA during wakefulness. The relationship of the sound features and known morphological upper airway structure of OSA subjects are also discussed.