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SST:一种打转移窗口变压器方法,用于潜在的阻塞性睡眠呼吸暂停患者诊断

Jing Luo^1,2, Yinuo Zhao^1,2, Haiqin Liu³

¹Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, 710048, People's Republic of China.

Physiological measurement

|February 5, 2024

概括

使用转移窗口变压器模型的新型非接触式音频方法可以以85.9%的准确度检测阻塞性睡眠呼吸暂停 (OSA). 这种方法为潜在的OSA患者提供了传统的多睡眠学 (PSG) 的方便替代方案.

科学领域:

生物医学工程生物医学工程
睡眠医学睡眠医学
人工智能的人工智能

背景情况:

阻塞性睡眠呼吸暂停 (OSA) 是一种普遍且严重的健康状况.
传统的多睡眠学 (PSG) 用于OSA诊断是不方便的和侵入性的.
需要非接触式,方便的诊断方法.

研究的目的:

开发一种非接触方法,使用睡眠音频信号来诊断潜在的OSA患者.
改进现有的OSA诊断方法.

主要方法:

利用转移窗口变压器模型从全夜睡眠记录中检测打音频.
在广泛的音频数据集上训练了一个打检测模型.
从检测到的打音频中采用深度特征统计指标来训练用于OSA诊断的随机森林分类器.

主要成果:

拟议的打转移窗口变压器 (SST) 方法在分类OSA患者中实现了85.9%的准确性,85.3%的灵敏度和85.6%的精度.
性能指标显著超过了最先进的9.7% (精度),10.7% (灵敏度) 和7.9% (精度).
这项研究使用了305名潜在的OSA患者的自我收集数据集.

结论:

该SST方法显著提高了非接触式基于音频的OSA诊断的性能.
这种方法为潜在的OSA患者提供了一个有希望的自我诊断工具.
这些发现表明,侵入性诊断程序的需求可能会减少.

关键词:

在OSA诊断的诊断.多尺度的特征是多尺度的特征.打检测打检测打检测窗户注意力机制注意力机制

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