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卷积神经网络基于光电解剖学信号用于睡眠呼吸暂停综合征检测
Xinge Jiang1, YongLian Ren2, Hua Wu1
1School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China.
Frontiers in neuroscience
|August 7, 2023
概括
一种使用光聚缩图 (PPG) 信号和卷积神经网络的新方法可以准确地检测睡眠呼吸暂停综合征 (SAS),准确度超过90%. 这为诊断睡眠障碍提供了更舒适,更方便的方法.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 睡眠医学 睡眠医学
背景情况:
- 目前的睡眠障碍监测是复杂的,耗时的,不舒服的.
- 准确的诊断对于管理全球睡眠质量至关重要.
- 睡眠呼吸暂停综合征 (SAS) 需要改进检测方法.
研究的目的:
- 开发一种舒适方便的方法来识别睡眠呼吸暂停综合征.
- 为SAS检测利用光聚光系统学 (PPG) 信号.
- 为了评估SAS分类的深度学习模型的性能.
主要方法:
- 开发了一种一维卷积神经网络 (CNN) 模型.
- 美国有线电视新闻网使用20名健康人和39名SAS患者的PPG信号进行了训练.
- 进行了反干扰实验,以评估模型对噪声的强度.
主要成果:
- 美国有线电视新闻网 (CNN) 模型在分类睡眠呼吸暂停综合征方面取得了超过90%的准确性.
- 开发的方法证明了一定程度的抗干扰能力.
- 基于PPG的检测方法在便携式应用中显示出前景.
结论:
- 已经建立了一种使用PPG信号进行SAS检测的新,准确和方便的方法.
- 这些发现支持可穿戴技术对睡眠障碍监测的潜力.
- 这种方法可以显著提高睡眠呼吸暂停诊断的可访问性.
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