Automatic identifying OSAHS patients and simple snorers based on Gaussian mixture models

Objective. Snoring is a typical symptom of Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS). In this study, an effective OSAHS patient detection system based on snoring sounds is presented.Approach. The Gaussian mixture model (GMM) is proposed to explore the acoustic characteristics of snoring sounds throughout the whole night to classify simple snores and OSAHS patients respectively. A series of acoustic features of snoring sounds of are selected based on the Fisher ratio and learned by GMM. Leave-one-subject-out cross validation experiment based on 30 subjects is conducted to validation the proposed model. There are 6 simple snorers (4 male and 2 female) and 24 OSAHS patients (15 male and 9 female) investigated in this work. Results indicates that snoring sounds of simple snorers and OSAHS patients have different distribution characteristics. Main results. The proposed model achieves average accuracy and precision with values of 90.0% and 95.7% using selected features with a dimension of 100 respectively. The average prediction time of the proposed model is 0.134 +/- 0.005 s. Significance. The promising results demonstrate the effectiveness and low computational cost of diagnosing OSAHS patients using snoring sounds at home.