An OSAHS evaluation method based on multi-features acoustic analysis of snoring sounds

Snoring is the most direct symptom of obstructive sleep apnea hypopnea syndrome (OSAHS) and implies a lot of information about OSAHS symptoms. This paper aimed to identify OSAHS patients by analyzing acoustic features derived from overnight snoring sounds. Mel-frequency cepstral coefficients, 800 Hz power ratio, spectral entropy and other 10 acoustic features were extracted from snores, and Top-6 features were selected from the extracted 10 acoustic features by a feature selection algorithm based on random forest, then 5 kinds of machine learning models were applied to validate the effectiveness of Top-6 features on identifying OSAHS patients. The results showed that when the classification performance and computing efficiency were taken into account, the combination of logistic regression model and Top-6 features performed best and could successfully distinguish OSAHS patients from simple snorers. The proposed method provides a higher accuracy for evaluating OSAHS with lower computational complexity. The method has great potential prospect for the development of a portable sleep snore monitoring device. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study aimed to identify OSAHS patients by analyzing acoustic features derived from overnight snoring sounds. Top-6 features selected from 10 acoustic features showed the best performance in combination with logistic regression model, successfully distinguishing OSAHS patients from simple snorers, providing higher accuracy for evaluating OSAHS with lower computational complexity and great potential for developing a portable sleep snore monitoring device.