一种混合变异模式分解框架,用于使用阻抗心脏图进行增强的心脏输出估计
Priya Darshini Kumari1, Ksh Milan Singh1, Zefree Lazarus Mayaluri2
1Department of Electrical Engineering, National Institute of Technology Meghalaya, Meghalaya, 793108, India.
Scientific reports
|July 16, 2025
概括
这项研究引入了阻抗心脏图 (ICG) 信号的新的三阶段否定框架,显著提高了心脏输出估计的准确性. 该方法提高了信号质量,这对于诊断心血管疾病至关重要.
科学领域:
- 生物医学工程 生物医学工程
- 心血管生理学心血管生理学
- 信号处理 信号处理
背景情况:
- 准确的心脏输出 (CO) 估计对于管理心血管疾病至关重要.
- 阻抗心脏图 (ICG) 信号容易产生噪声器件,从而限制了可靠性.
- 现有的无声化方法在ICG数据中遇到复杂的噪声.
研究的目的:
- 为提高ICG信号质量,开发和验证一个新的三阶段拒绝的框架.
- 为了提高心脏输出估计的准确性和稳定性,从无声化ICG信号.
- 根据最先进的方法评估框架的性能及其临床可行性.
主要方法:
- 一个三阶段的否定框架,集成变量模式分解 (VMD),非局部平均值 (NLM) 和离散波形变换 (DWT).
- 在ReBeatICG数据集上进行验证,包括带有运动文物和基线漂移的信号.
- 使用信号与噪声比 (SNR),平均平方误差 (MSE),百分比根平均平方差 (PRD),F1得分和Denoising稳定性指数 (DRI) 等指标进行性能评估.
主要成果:
- 与两阶段方法相比,实现了高达1.2dB的SNR改善,并将MSE降低了13%,PRD降低了9%.
- 增强的信任点检测 (高达4.4%的F1分数增加) 和保持心率变化 (HRV) 忠实性 (0.91相关系数).
- 在各种噪声条件下,经过统计验证,证明了优越的无噪声强度和信号保真性,在各种噪声条件下保持信号保真.
结论:
- 拟议的VMD-NLM-DWT框架显著提高了ICG信号质量,以进行可靠的心脏输出估计.
- 该方法在保持临床相关的信号特征和准确性方面优于现有技术.
- 计算效率支持临床和门诊心血管监测中的实时应用.
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