Processing of impedance cardiogram differential for non-invasive cardiac function detection.
10.7507/1001-5515.201804014
- Author:
Yadan ZHANG
1
;
Zhong JI
2
,
3
;
Xia TAN
1
;
Zhe SHEN
4
;
Lianjiao XU
5
Author Information
1. College of biological Engineering, Chongqing University, Chongqing 400044, P.R.China.
2. College of biological Engineering, Chongqing University, Chongqing 400044, P.R.China
3. Chongqing Medical Electronics Engineering Technology Center, Chongqing 400044, P.R.China.jizhong@cqu.edu.cn.
4. Henan Province Medical Instrument Testing Institute, Zhengzhou 450008, P.R.China.
5. Chongqing Armed Corps Police Hospital, Chongqing 400061, P.R.China.
- Publication Type:Journal Article
- Keywords:
adaptive ensemble empirical mode decomposition;
feature detection;
impedance cardiogram differential;
non-invasive cardiac function detection;
preprocessing
- From:
Journal of Biomedical Engineering
2019;36(1):50-58
- CountryChina
- Language:Chinese
-
Abstract:
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.
