LAUSR

OBJECTIVE Successful surgical treatment of obstructive sleep apnea (OSA) depends on precisely locating the tissue of vibration. Snoring is the main symptom of OSA and can be utilized to detect the active location of tissues. However, existing approaches are limited owing to their inability to capture the characteristics of snoring produced from the upper airway. This paper proposes a new approach to better distinguish different snoring sounds that are generated from four different excitation locations. APPROACH First, we propose a robust null space pursuit (RNSP) algorithm for extracting the trend from the amplitude spectrum of snoring. Second, a new feature from this extracted amplitude spectrum trend, which outperforms the Mel-frequency cepstral coefficients (MFCC) feature, is designed. Subsequently, the newly proposed features, namely the trend-based MFCC (TCC), are reduced in dimensionality by using principle component analysis. Finally, a support vector machine is employed for the classification task. MAIN RESULTS By using the TCC, the proposed approach achieves an unweighted average recall of 87.5% on the classification of four excitation locations on the public dataset MPSSC. SIGNIFICANCE The TCC is a promising feature for capturing the characteristics of snoring. The proposed method performs effective snore classification and can provide assistance for accurate OSA diagnosis.