Correlation analysis of sleep breathing sound and polysomnographic features

OBJECTIVE: To find relationship between sleep breathing sounds and various polysomnographic features. METHOD: Patients who underwent PSG with audio recording using a microphone were included. After noise reduction preprocessing, the data were segmented into 5-second windows and sound features were extracted. A simple linear regression of various sound features were performed with various PSG features including apnea hypopnea index (AHI), apnea index (AI), hypopnea index (HI), Oxygen desaturation index (ODI), and respiratory arousal (RA). Also prediction of PSG features from sleep breathing sounds had been established using machine learning. RESULT: A total of 116 subjects were included. A total of 508 sound features were extracted. Among them, Derivative of Area Method of Moments Overall Standard Deviation was the most distinguishing feature that explained AHI (R2= 0.628), AI (R2=0.620), ODI (R2= 0.641), SpO2 CONCLUSION: Similar prediction values were acquired with simple linear regression and machine learning. Especially machine learning resulted in a better performance regarding to AI and Time of SpO2