使用呼吸信号对睡眠呼吸障碍进行算法检测:系统性审查
Liqing Yang1,2, Zhimei Ding1,2, Jiangjie Zhou2,3
1Key Laboratory of Biorheological Science and Technology of Ministry of Education, Chongqing University, Chongqing, People's Republic of China.
Physiological measurement
|February 22, 2024
概括
这篇关于睡眠呼吸障碍 (SDB) 检测算法的综述强调了鼻腔气流信号,达到94.11%的准确性. 呼吸运动信号也显示出确定SDB类型的希望.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
- 信号处理 信号处理
背景情况:
- 睡眠呼吸障碍 (SDB) 与高血压和糖尿病等严重健康问题有关.
- 多睡眠学 (PSG) 是标准的诊断工具,但成本昂贵,耗时,导致诊断不足.
- 使用单信号的成本效益高的算法为更广泛的SDB选提供了潜在的解决方案.
研究的目的:
- 系统地审查2012-2022年关于使用呼吸信号检测SDB算法的文献.
- 分析信号源,处理技术,特征提取和分类方法.
- 确定研究缺口,并为未来的SDB诊断研究提供参考.
主要方法:
- 按照PROSPERO协议进行的系统文献审查 (CRD42022385130).
- 选了342篇论文,其中32篇研究被选为数据提取.
- 分析了各种呼吸信号源,算法方法和性能指标.
主要成果:
- 呼吸信号包括鼻气流 (NAF),口鼻气流 (OAF) 和呼吸运动 (TRE,ARE).
- 鼻腔空气流算法实现了最高的平均精度 (94.11%).
- 通常使用值,机器学习和深度学习模型;TRE/ARE信号有效识别了SDB类型.
结论:
- 许多SDB检测算法存在,其准确性取决于信号源,处理,特性和模型.
- 虽然NAF显示了高准确度,但单个信号的低呼吸检测灵敏度仍然适度.
- 呼吸运动信号 (TRE,ARE) 提供可靠的SDB鉴定,由于不同的运动模式.
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