Improving OSAHS Prevention Based on Multidimensional Feature Analysis of Snoring

Abstract

Obstructive Sleep Apnea–Hypopnea Syndrome (OSAHS), a severe respiratory sleep disorder, presents a significant threat to human health and even endangers life. As snoring is the most noticeable symptom of OSAHS, identifying OSAHS via snoring sound analysis is vital. This study aims to analyze the time-domain and frequency-domain characteristics of snoring sounds to detect OSAHS and its severity. The snoring sounds are extracted and scrutinized from nighttime acoustic signals, with spectral energy ratio features being applied, calculated via the snore detection frequency division method. A variety of time and frequency-domain features are derived from the snoring sounds. A novel Snore Detection Cepstral Coefficient (SDCC) is proposed, based on Mel Frequency Cepstral Coefficients (MFCCs) and snore detection frequency division. Relief-F feature screening is then applied to SDCC and MFCC. Canonical Correlation Analysis (CCA) is utilized on the fusion features obtained as a result, and the results indicate the highest accuracy (97.8%) with Subspace KNN. The optimal classifier with feature combination is used for the snore model of OSASH early warnings all night, effectively recognizing and assessing OSAHS and reflecting the severity of its disease. This result, achieving high accuracy and low computational complexity, shows that the proposed method holds significant promise for developing portable sleep health detection devices.