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相关概念视频

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Pulse rhythm01:30

Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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相关实验视频

Updated: Jul 18, 2025

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
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Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

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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
PubMed
概括
此摘要是机器生成的。

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.

关键词:
环境光视频环境光视频心率是指心率是如何发生的.氧气水平 氧气水平预测 预测 预测 预测呼吸速率 呼吸速率 呼吸速率 呼吸速率验证验证的时间基于视频的监控监控.重要标志 重要标志

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Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
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Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
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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.