Amplitude spectrum trend-based feature for excitation location classification from snore sounds

Abstract

Objective: Successful surgical treatment of obstructive sleep apnea (OSA) depends on the precise location of the vibrating tissue. 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 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 coefficient (MFCC) feature, is designed. Subsequently, the newly proposed feature, namely the trend-based MFCC (TCC), is reduced in dimensionality by using principal 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 Munich Passau Snore Sound Corpus. Significance: The TCC is a promising feature for capturing the characteristics of snoring. The proposed method can effectively perform snore classification and assist in accurate OSA diagnosis.