Motion impedance cardiography denoising method based on canonical correlation analysis and coherence analysis

被引:1
作者
Xie, Yao [1 ,3 ]
Yu, Honglong [2 ,3 ]
Xie, Qilian [3 ,4 ]
机构
[1] Univ Sci & Technol China, Sch Engn Sci, Hefei, Peoples R China
[2] Hefei Univ Technol, Dept Biomed Engn, Hefei, Peoples R China
[3] Anhui Tongling Bion Technol Co Ltd, Hefei, Peoples R China
[4] Anhui Med Univ, Hefei, Peoples R China
关键词
Impedance cardiography; Artifacts; Canonical correlation analysis; Coherence analysis; CARDIAC-OUTPUT; STROKE VOLUME; PARAMETERS; FILTER; TOOL;
D O I
10.1016/j.bspc.2023.105300
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Impedance cardiography (ICG) is an attractive noninvasive method for measuring stroke volume and cardiac output. However, it is easily disturbed by the artifacts such as respiration and body shaking. Considering the correlation between physiological and motion signals, and the synchronization relationship among the physiological signals, this paper introduces an ICG denoising method based on canonical correlation analysis (CCA) and coherence analysis to remove the artifacts during the measurement. First, the CCA extracts the shared components between the electrocardiogram (ECG) and the motion signal, as well as the shared components between ICG and the motion signal. Then, set those shared components to zero to suppress the primary motion artifacts. Next, the coherence analysis was used to calculate the synchronization relationship between obtained ECG and ICG components. Finally, the components with strong synchronization relationships were used to reconstruct the ICG signal. The denoising method was evaluated for 54 subjects during lying and walking. Experimental results show that after removing the artifacts, the signal quality index beat contribution factor (BCF) was increased from the original 78.1% to 97.8%, and the physiological parameters measured based on the proposed method were in good agreement with those measured by the standard instrument. The proposed denoising method effectively improves the reliability of analysis and diagnosis on cardiovascular diseases relying on ICG signals.
引用
收藏
页数:11
相关论文
共 50 条
[41]   Robust gait recognition based on partitioning and canonical correlation analysis [J].
Luo, Can ;
Xu, Wanjiang ;
Zhu, Canyan .
2015 IEEE INTERNATIONAL CONFERENCE ON IMAGING SYSTEMS AND TECHNIQUES (IST) PROCEEDINGS, 2015, :269-273
[42]   Blood Glucose Concentration Prediction Based on Canonical Correlation Analysis [J].
He, Jinli ;
He, Tong ;
Wang, Youqing .
PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC), 2019, :2942-2947
[43]   Point Cloud Registration Algorithm Based on Canonical Correlation Analysis [J].
Tang Z. ;
Liu M. ;
Jiang Y. ;
Zhao F. ;
Zhao C. .
Zhongguo Jiguang/Chinese Journal of Lasers, 2019, 46 (04)
[44]   Unsupervised discriminant canonical correlation analysis based on spectral clustering [J].
Wang, Sheng ;
Lu, Jianfeng ;
Gu, Xingjian ;
Weyori, Benjamin A. ;
Yang, Jing-yu .
NEUROCOMPUTING, 2016, 171 :425-433
[45]   Asymptotic distributions in the projection pursuit based canonical correlation analysis [J].
JIN Jiao CUI HengJian Department of Statistics and Financial Mathematics School of Mathematical Sciences Beijing Normal University Laboratory of Mathematics and Complex Systems Beijing Normal University Ministry of Education Beijing China .
ScienceChina(Mathematics), 2010, 53 (02) :485-498
[46]   Point Cloud Registration Algorithm Based on Canonical Correlation Analysis [J].
Tang Zhirong ;
Liu Mingzhe ;
Jiang Yue ;
Zhao Feixiang ;
Zhao Chengqiang .
CHINESE JOURNAL OF LASERS-ZHONGGUO JIGUANG, 2019, 46 (04)
[47]   Metacognition by gender: A pilot Study based on canonical correlation analysis [J].
Mucciardi, Massimo ;
Lenzo, Vittorio ;
Toffle, Mary Ellen ;
Quattropani, Maria C. .
CPSYC 2016 - INTERNATIONAL CONGRESS ON CLINICAL AND COUNSELLING PSYCHOLOGY, 2016, 9 :41-52
[48]   A Jacobi-Davidson Method for Large Scale Canonical Correlation Analysis [J].
Teng, Zhongming ;
Zhang, Xiaowei .
ALGORITHMS, 2020, 13 (09)
[49]   The group sparse canonical correlation analysis method in the imaging genetics research [J].
Wu, Jie ;
Xu, Jiawei ;
Chen, Wei ;
Sun, Deyan .
2020 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, 2020, :2554-2557
[50]   Functional connectivity analysis of fMRI data based on regularized multiset canonical correlation analysis [J].
Deleus, Filip ;
Van Hulle, Marc M. .
JOURNAL OF NEUROSCIENCE METHODS, 2011, 197 (01) :143-157