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超敏感的在线NO传感器基于分布式并行自我调节的神经网络和紫外差异光学吸收光谱仪,用于呼气诊断

Rui Zhu¹, Jie Gao¹, Mu Li¹

¹School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.

|February 21, 2024

概括

一个新型的气体传感器系统准确地检测到分数排气的氧化 (FeNO) 在ppb水平. 这项技术有助于通过独特的神经网络模型和紫外线光谱学来诊断炎症性呼吸道疾病.

科学领域:

生物医学工程生物医学工程
分析化学分析化学
频谱学是一种光谱学.

背景情况:

分数呼出的氧化 (FeNO) 度是呼吸道炎症的关键指标.
精确的FeNO检测对于诊断炎症性呼吸道疾病至关重要.

研究的目的:

开发一个高精度的气体传感器系统,用于检测ppb级的FeNO度.
提高用于临床诊断的FeNO检测的准确性和可靠性.

主要方法:

利用紫外差光学吸收光谱 (UV-DOAS) 与分布式并行自我调节神经网络 (DPSRNN) 模型相结合.
在光谱数据中使用离散波段变换来消除噪声信号.
开发了一个DPSRNN模型,专注于NOUV吸收光谱内的多峰特征吸收结构.

主要成果:

在2.59-750.66ppb的范围内实现了NO的高精度检测.
证明了0.17ppb的低平均绝对误差和0.84%的测量精度.
成功验证了传感器系统,用于在呼气分析中高精度在线检测FeNO.

结论:

通过考虑个体吸收特征区域,DPSRNN模型在FeNO度预测方面优于传统的CNN模型.
开发的传感器系统代表了重大进步,为FeNO检测取得了迄今为止最好的结果.
该系统显示出非侵入性诊断和监测炎症性呼吸道疾病的巨大潜力.

关键词:

离散的波形变换.部分排出的氧化氧化物.气体传感器是一个气体传感器.神经网络的神经网络的神经网络紫外线差分吸收光谱学紫外线差分吸收光谱学

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