Sleep Apnea Detection Based on Snoring Sound Analysis Using DS-MS neural network

Abstract

Sleep apnea hypopnea syndrome (OSAHS) is a high-incidence disease with serious harm and potential dangers. Currently, the traditional scheme for monitoring sleep quality mainly focuses on monitoring two physiological signals: electroencephalogram (EEG) and heartbeat. However, in the sleep state, respiration is also an important physiological signal. This paper proposes a sleep apnea detection method based on snoring sound analysis using deep learning. Firstly, snoring sound signals are preprocessed and feature extraction is performed using Mel-frequency cepstral coefficients (MFCC). The extracted features are then used to train a DS-MS neural network model, and the optimal detection model is obtained through iterations. The experimental results show that the accuracy of the proposed detection model can reach 94.17%.