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在多人场景中无接触检测异常呼吸,使用正交频率分裂多重复合信号和深度学习

Muneeb Ullah¹, Xiaodong Yang², Zhiya Zhang²

¹Key Laboratory of High Speed Circuit Design and EMC of Ministry of Education, School of Electronic EngineeringXidian University Xi'an Shaanxi 710071 China.

IEEE open journal of engineering in medicine and biology

|February 5, 2025

概括

这项研究引入了使用软件定义无线电 (SDR) 和深度学习的无接触系统,以准确检测和分类11种异常呼吸模式,即使在多人场景中也是如此. VGG16-GRU模型实现了99%以上的准确性,用于实时呼吸监测.

科学领域:

生物医学工程生物医学工程
信号处理信号处理
人工智能的人工智能

背景情况:

无接触检测异常呼吸模式是困难的,特别是当有多个个体时.
软件定义无线电 (SDR) 显示了捕捉微妙呼吸运动的潜力,但多人干扰使分析复杂化.
区分个体的呼吸模式在近距离或与类似的条件仍然是一个挑战.

研究的目的:

开发一种非接触式,非侵入性系统,用于监测和分类异常呼吸模式.
在呼吸监测中应对多人场景的挑战.
通过先进的信号处理和深度学习,实现各种呼吸道疾病的实时分类.

主要方法:

使用SDR技术捕获的直角频率分割复杂化 (OFDM) 信号.
开发了一种混合深度学习模型 (VGG16-GRU),将CNN和GRU结合起来用于时空特征提取.
在办公环境中收集数据集,包括复杂的多主题场景.

主要成果:

该系统准确地检测和分类了11种不同的呼吸模式 (例如,百日咳,呼吸暂停,Cheyne-Stokes,OSA).
实现了高性能指标:99.07%的整体准确率,99.08%的精度,99.09%的回忆率和99.07%的F1分数.
在多人环境中区分个体呼吸模式的证明有效性.

结论:

这项研究提供了一个可靠和可扩展的解决方案,用于非接触式,实时的呼吸系统疾病检测和分类.
该系统在临床和医疗保健环境中具有用于自动诊断工具的巨大潜力.
未来的工作包括数据集的扩展和对各种现实呼吸系统数据的模型改进.

关键词:

不正常的呼吸模式.特别提款权 (SDR) 的信号深度学习技术深度学习技术呼吸道疾病的诊断呼吸道疾病的诊断

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