Monitoring of Sleep Breathing States Based on Audio Sensor Utilizing Mel-Scale Features in Home Healthcare

Yu Fang; Dongbo Liu; Zhongwei Jiang; Haibin Wang

doi:10.1155/2023/6197564

Monitoring of Sleep Breathing States Based on Audio Sensor Utilizing Mel-Scale Features in Home Healthcare

J Healthc Eng. 2023 Feb 9:2023:6197564. doi: 10.1155/2023/6197564. eCollection 2023.

Authors

Yu Fang^{1

2}, Dongbo Liu^{1

2}, Zhongwei Jiang², Haibin Wang¹

Affiliations

¹ School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610000, China.
² Graduate School of Science and Technology for Innovation, Yamaguchi University, Ube 755-0096, Japan.

Abstract

Sleep-related breathing disorders (SBDs) will lead to poor sleep quality and increase the risk of cardiovascular and cerebrovascular diseases which may cause death in serious cases. This paper aims to detect breathing states related to SBDs by breathing sound signals. A moment waveform analysis is applied to locate and segment the breathing cycles. As the core of our study, a set of useful features of breathing signal is proposed based on Mel frequency cepstrum analysis. Finally, the normal and abnormal sleep breathing states can be distinguished by the extracted Mel-scale indexes. Young healthy testers and patients who suffered from obstructive sleep apnea are tested utilizing the proposed method. The average accuracy for detecting abnormal breathing states can reach 93.1%. It will be helpful to prevent SBDs and improve the sleep quality of home healthcare.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Delivery of Health Care
Humans
Polysomnography / methods
Respiration*
Sleep
Sleep Apnea, Obstructive* / diagnosis