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Snoring Sounds Classification of OSAHS Patients Based on Model Fusion

Luo Yexin, Peng Jianxin, Ding Li, Zhang Yikai, Song Lijuan, Zhang Qianfan, Chen Houpeng

Archives of Acoustics

2025

Vol. 50, nr 1

69--79

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a prevalent and detrimental chronic condition.The conventional diagnostic approach for OSAHS is intricate and costly. Snoring is one of the most typicaland easily obtained symptom of OSAHS patients. In this study, a series of acoustic features are extractedfrom snoring sounds. A fused model that integrates a deep neural network, K-nearest neighbors (KNN), anda random under sampling boost algorithm is proposed to classify snoring sounds of simple snorers (SSSS),simple snoring sounds of OSAHS patients (SSSP), and apnea-hypopnea snoring sounds of OSAHS patients(APSP). The ReliefF algorithm is employed to select features with high relevance in each classification model.A hard voting strategy is implemented to obtain an optimal fused model. Results show that the proposed fusedmodel achieves commendable performance with an accuracy rate of 85.76 %. It demonstrates the effectivenessand validity of assisting in diagnosing OSAHS patients based on the analysis of snoring sounds.