Automated sleep apnea detection in snoring signal using long short-term memory neural networks

Abstract

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a high incidence disease with serious hazard and potential danger. The polysomnography(PSG) has become the gold standard to diagnose OSAHS. However, the PSG is limited for household use because of its operational complexity, technical nature and high consumption.Currently, Microphone, as a non-contacting tensor, is an alternative method. And researchers devoted to analyze respiratory sound for detecting and evaluating OSAHS patients. In this paper, a classifier based on Long Short-Term Memory (LSTM) is proposed to identify the respiratory event-related snoring from simple snoring. Firstly, we collected the sleep sound of 33 patients and 10 normal people from the hospital. And 4780 abnormal snoring segments and 10,740 normal snoring segments were recorded by Mics. Then Mel-frequency cepstrum coefficients(MFCC), Mel Filter Banks (Fbanks), Short-time Energy and Linear Prediction Coefficient(LPC), representing the different characteristics of snoring, are extracted as characteristic features of snoring.At last, a multi-input model based on LSTM is designed, which can receive various audio features to synthesize information to identify snoring. Compared with single feature network processing, the use of multiple feature coefficients can identify the features of snoring at a fine-grained level. In the experiment, our method could classify respiratory event related snoring and normal snoring at accuracy 95.3%, and the accuracy of the three-category snore related to the severity of OSAHS can reach 81.6%. The recognition results can be used for the auxiliary diagnosis of OSAHS.