Detection of Snore from OSAHS Patients Based on Deep Learning.

Fanlin Shen,Zhu Li,Keqiang Yue,Lili Dai,Wenjun Li,Siyi Cheng,Jiafeng Yao

doi:10.1155/2020/8864863

Abstract

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is extremely harmful to the human body and may cause neurological dysfunction and endocrine dysfunction, resulting in damage to multiple organs and multiple systems throughout the body and negatively affecting the cardiovascular, kidney, and mental systems. Clinically, doctors usually use standard PSG (Polysomnography) to assist diagnosis. PSG determines whether a person has apnea syndrome with multidimensional data such as brain waves, heart rate, and blood oxygen saturation. In this paper, we have presented a method of recognizing OSAHS, which is convenient for patients to monitor themselves in daily life to avoid delayed treatment. Firstly, we theoretically analyzed the difference between the snoring sounds of normal people and OSAHS patients in the time and frequency domains. Secondly, the snoring sounds related to apnea events and the nonapnea related snoring sounds were classified by deep learning, and then, the severity of OSAHS symptoms had been recognized. In the algorithm proposed in this paper, the snoring data features are extracted through the three feature extraction methods, which are MFCC, LPCC, and LPMFCC. Moreover, we adopted CNN and LSTM for classification. The experimental results show that the MFCC feature extraction method and the LSTM model have the highest accuracy rate which was 87% when it is adopted for binary-classification of snoring data. Moreover, the AHI value of the patient can be obtained by the algorithm system which can determine the severity degree of OSAHS.

Highlights

IntroductionObstructive apnea hypopnea syndrome (hereinafter called Obstructive sleep apnea-hypopnea syndrome (OSAHS)) leads to poor sleep quality and leads to chronic hypoxemia, hypercapnia, and even high-grade central nervous system dysfunction lesions, which brings great negative impact to people. erefore, in order to analyze the reason and sum up the diagnosis method and response treatment policy of OSAHS, more and more researchers are devoted to the research of the disease [1,2,3]
Obstructive apnea hypopnea syndrome leads to poor sleep quality and leads to chronic hypoxemia, hypercapnia, and even high-grade central nervous system dysfunction lesions, which brings great negative impact to people. erefore, in order to analyze the reason and sum up the diagnosis method and response treatment policy of Obstructive sleep apnea-hypopnea syndrome (OSAHS), more and more researchers are devoted to the research of the disease [1,2,3]
Sleep sounds are collected from 32 volunteers through a microphone for a whole night (8 h) at a sampling frequency of 16 KHz. e device can be used at home

Summary

Introduction

Obstructive apnea hypopnea syndrome (hereinafter called OSAHS) leads to poor sleep quality and leads to chronic hypoxemia, hypercapnia, and even high-grade central nervous system dysfunction lesions, which brings great negative impact to people. erefore, in order to analyze the reason and sum up the diagnosis method and response treatment policy of OSAHS, more and more researchers are devoted to the research of the disease [1,2,3]. Obstructive apnea hypopnea syndrome (hereinafter called OSAHS) leads to poor sleep quality and leads to chronic hypoxemia, hypercapnia, and even high-grade central nervous system dysfunction lesions, which brings great negative impact to people. Polysomnography (hereinafter called PSG) is used to assist doctors in diagnosing OSAHS. PSG affects the quality of sleep because the user needs to plug in the corresponding equipment in many parts of the body during use. If there is a technology that does not rely on large medical equipment and is comfortable for patients in daily use, it can improve the experience of patients when they are monitored and help doctors more accurately grasp the long-term clinical performance of patients

Methods

Results

Discussion

Conclusion