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2025 , Vol. 42 >Issue 4: 538 - 546

DOI: https://doi.org/10.7523/j.ucas.2023.066

简报

基于热成像的非接触式睡眠呼吸暂停检测与分类

  • 廖楚楚 ,
  • 黄志蓓 ,
  • 秦飞 ,
  • 王奕权 ,
  • 王涛 ,
  • 童永刚
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  • 中国科学院大学电子电气与通信工程学院, 北京 101408

收稿日期: 2023-03-14

  修回日期: 2023-05-26

  网络出版日期: 2023-05-26

基金资助

中央高校基本科研业务费专项资助

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Non-contact sleep apnea detection and classification using thermal imaging

  • LIAO Chuchu ,
  • HUANG Zhipei ,
  • QIN Fei ,
  • WANG Yiquan ,
  • WANG Tao ,
  • TONG Yonggang
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  • School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences,Beijing 101408, China

Received date: 2023-03-14

  Revised date: 2023-05-26

  Online published: 2023-05-26

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摘要

睡眠呼吸暂停综合征是一种常见且潜在危害巨大的睡眠疾病，睡眠呼吸暂停的分类检测可为疾病诊断提供重要依据。基于视频的睡眠监测系统因其非接触性，适用于该疾病的普适性筛查，其中热像仪隐私保护性强，近年来开始受到广泛关注。提出一种基于热成像的睡眠呼吸暂停检测与分类方法，通过采集胸腹部运动的时序信息，构建不同生理机制映射下中枢性和阻塞性睡眠呼吸暂停的二维复特征空间，并基于其统计特性提出呼吸努力强度特征与呼吸努力异同度特征，进而实现这2类睡眠呼吸暂停的分类检测。实验结果表明2类睡眠呼吸暂停检测的准确率均超过97.0%，有效克服了视频中观测噪声和冗余信息导致的有效信息难以提取等问题，有望助力于睡眠疾病的实际筛查诊断。

关键词： 睡眠呼吸暂停分类; 胸腹部运动; 热成像; 呼吸努力强度; 呼吸努力异同度

本文引用格式

廖楚楚 , 黄志蓓 , 秦飞 , 王奕权 , 王涛 , 童永刚 . 基于热成像的非接触式睡眠呼吸暂停检测与分类[J]. 中国科学院大学学报, 2025 , 42(4) : 538 -546 . DOI: 10.7523/j.ucas.2023.066

Abstract

Sleep apnea syndrome is a common and potentially harmful sleep disorder, and the classification and detection of sleep apnea can provide an important basis for the diagnosis of the disease. Due to their non-contact nature, video-based sleep monitoring systems are universally applicable for disease screening, among which thermal imaging cameras, with strong privacy protection, have attracted wide attention in recent years. In this paper, we propose a novel sleep apnea detection and classification method using thermal imaging. By obtaining the temporal information of thoracic and abdominal movement, a two-dimensional complex feature space mapping central and obstructive sleep apnea under different physiological mechanisms is constructed. Based on their statistical properties, the respiratory effort intensity feature and the respiratory effort asynchrony feature are proposed to achieve the classification and detection of two types of sleep apnea. Experimental results show that the accuracy of detecting both types of sleep apnea exceeds 97.0%. This work effectively overcomes the problem of difficulty in extracting valid information caused by observation noise and redundant information in videos, and is expected to assist in the actual screening and diagnosis of sleep disorders.

Key words： sleep apnea classification; thoracic and abdominal movement; thermal imaging; respiratory effort intensity; respiratory effort asynchrony

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