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基于两步谱总体经验模态分解和典型相关分析的运动阻抗心动图去噪方法研究

[Research on motion impedance cardiography de-noising method based on two-step spectral ensemble empirical mode decomposition and canonical correlation analysis].

作者信息

Xie Yao, Yang Dong, Yu Honglong, Xie Qilian

机构信息

School of Information Science and Technology, University of Science and Technology of China, Hefei 230022, P. R. China.

Anhui Tongling Bionic Technology Co. Ltd, Hefei 230601, P. R. China.

出版信息

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Oct 25;41(5):986-994. doi: 10.7507/1001-5515.202210059.

Abstract

Impedance cardiography (ICG) is essential in evaluating cardiac function in patients with cardiovascular diseases. Aiming at the problem that the measurement of ICG signal is easily disturbed by motion artifacts, this paper introduces a de-noising method based on two-step spectral ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA). Firstly, the first spectral EEMD-CCA was performed between ICG and motion signals, and electrocardiogram (ECG) and motion signals, respectively. The component with the strongest correlation coefficient was set to zero to suppress the main motion artifacts. Secondly, the obtained ECG and ICG signals were subjected to a second spectral EEMD-CCA for further denoising. Lastly, the ICG signal is reconstructed using these share components. The experiment was tested on 30 subjects, and the results showed that the quality of the ICG signal is greatly improved after using the proposed denoising method, which could support the subsequent diagnosis and analysis of cardiovascular diseases.

摘要

阻抗心动图(ICG)在评估心血管疾病患者的心功能方面至关重要。针对ICG信号测量容易受到运动伪影干扰的问题,本文介绍了一种基于两步谱总体经验模态分解(EEMD)和典型相关分析(CCA)的去噪方法。首先,分别在ICG与运动信号、心电图(ECG)与运动信号之间进行第一次谱EEMD-CCA。将相关系数最强的分量设为零,以抑制主要运动伪影。其次,对得到的ECG和ICG信号进行第二次谱EEMD-CCA以进一步去噪。最后,利用这些共享分量重建ICG信号。该实验在30名受试者身上进行测试,结果表明,使用所提出的去噪方法后,ICG信号质量得到了极大改善,可为后续心血管疾病的诊断和分析提供支持。

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