基于远程光电解质谱的人类生命体征预测,使用循环算法
Kapil Gupta1, Ruchika Sinhal2, Sagarkumar S Badhiye3
1Department of Computer Engineering, St. Vincent Pallotti College of Engineering and Technology, Nagpur, India.
Journal of biophotonics
|August 24, 2023
概括
这项研究使用环境光视频准确预测心率,呼吸率和氧和. 先进的图像处理和算法使无接触式生命体征监测成为可能,即使在具有挑战性的低光条件下也是如此.
科学领域:
- 生物医学工程 生物医学工程
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 远程光电脉冲扫描 (iPPG) 提供了一种非接触式方法来监测生命体征.
- 现有的iPPG技术面临着环境光变化和运动工件的挑战.
- 准确的生命体征评估对于远程患者监测和医疗保健至关重要.
研究的目的:
- 使用环境光视频开发一个可靠的系统来预测心率 (HR),呼吸率 (RR) 和动脉氧和度 (SpO2).
- 为了提高图像质量和克服低光视频中的扭曲,以改善生命体征提取.
- 为了验证系统的准确性和远程,非侵入性患者监测的潜力.
主要方法:
- 采用级剩余CNN-FPNR技术进行图像预处理和通过能量差异最大化来增强信号与噪声比 (SNR).
- 采用图像级联网络 (ICNet) 进行有效的细分,特别是在低光条件下.
- 实现了一个非接触算法,将主要组件分析 (PCA) 和快速里叶变换 (FFT) 结合起来,用于HR和RR评估,并进行动态时间扭曲以减少运动工件.
- 引入了基于强度方差的值分析,用于SpO2的确定和支持向量机 (SVM) 用于地面真实验证.
主要成果:
- 使用ICNet. 在低光环境视频中实现了强大的细分性能.
- 成功地通过iPG通过非侵入性预测HR和RR.
- 展示了用于确定SpO2水平的创新方法.
- 在远程生命体征评估中展示了有希望的准确性,减轻了非自愿运动带来的挑战.
结论:
- 开发的系统显示了使用环境光视频进行准确和远程生命体征监测的巨大潜力.
- 集成先进的图像处理,信号分析和机器学习技术增强了对环境挑战的强度.
- 这种非接触式方法为各种医疗保健环境中的传统生命体征测量方法提供了可行的替代方案.
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