Abstract
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.
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