Abstract：
Impedance cardiography (ICG) plays a crucial role in clinically evaluating cardiac systolic and diastolic functions, along with various other cardiac parameters. However, its accuracy heavily depends on precisely identifying feature points reflecting cardiac function. Moreover, traditional signal processing techniques used to mitigate random noise and breathing artifacts may inadvertently distort the amplitude and temporal characteristics of ICG signals. To address this issue, this study investigates a noise and artifact elimination method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Particle Swarm Optimization-based Variational Mode Decomposition Algorithm (PSO-VMD). The goal is to preserve the amplitude and temporal features of ICG signals to ensure accurate feature point extraction and computation of associated cardiac parameters. Comparative analysis with signal processing methods employing various wavelet families and Ensemble Empirical Mode Decomposition (EEMD) in ICG signal processing applications reveals that the proposed method achieves superior signal-to-noise ratio (SNR) and lower root-mean-square error (RMSE), while demonstrating enhanced correlation and waveform consistency with the original signal.
摘要：
阻抗心动图（ICG）在临床评估心脏收缩和舒张功能中起着至关重要的作用。以及其他各种心脏参数。然而,其准确性在很大程度上取决于精确识别反映心脏功能的特征点。此外，用于减轻随机噪声和呼吸伪影的传统信号处理技术可能会无意中扭曲ICG信号的幅度和时间特性。为了解决这个问题，本研究研究了一种基于改进的具有自适应噪声的完整集合经验模态分解（ICEEMDAN）和基于粒子群优化的变分模态分解算法（PSO-VMD）的噪声和伪影消除方法。目标是保留ICG信号的幅度和时间特征,以确保准确的特征点提取和相关心脏参数的计算。在ICG信号处理应用中，与采用各种小波族和集合经验模态分解（EEMD）的信号处理方法的比较分析表明，所提出的方法具有出色的信噪比（SNR）和较低的均方根误差（RMSE）。同时证明与原始信号的相关性和波形一致性增强。
