生物医学工程学杂志

生物医学工程学杂志

用于无创心功能检测的心阻抗微分信号处理

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基于胸部生物阻抗技术开展对心脏血流动力学参数准确计算的前提是心阻抗微分信号特征点的准确识别。为提高心阻抗微分信号特征点识别的准确率,本文首先设计了自适应集合经验模态和小波阈值降噪技术相结合的信号预处理方法,然后根据自适应集合经验模态分解的结果,结合差分法和自适应分段技术定位了心阻抗微分信号中的 A、B、C 和 X 点。本研究以临床上采集到的 30 例病理性心阻抗微分信号为例,对本文所提算法的特征检测准确度进行检验。研究结果显示,本文所提算法对信号特征的准确识别率总体可达 99.72%,进一步保证了基于胸部生物阻抗技术的心脏血流动力学参数的计算准确度。

The precise recognition of feature points of impedance cardiogram (ICG) is the precondition of calculating hemodynamic parameters based on thoracic bioimpedance. To improve the accuracy of detecting feature points of ICG signals, a new method was proposed to de-noise ICG signal based on the adaptive ensemble empirical mode decomposition and wavelet threshold firstly, and then on the basis of adaptive ensemble empirical mode decomposition, we combined difference and adaptive segmentation to detect the feature points, A, B, C and X, in ICG signal. We selected randomly 30 ICG signals in different forms from diverse cardiac patients to examine the accuracy of the proposed approach and the accuracy rate of the proposed algorithm is 99.72%. The improved accuracy rate of feature detection can help to get more accurate cardiac hemodynamic parameters on the basis of thoracic bioimpedance.

关键词: 自适应集合经验模态分解; 心阻抗微分信号; 特征点识别; 预处理; 无创心功能检测

Key words: adaptive ensemble empirical mode decomposition; impedance cardiogram differential; feature detection; preprocessing; non-invasive cardiac function detection

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