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Motion robust ICG measurements using a two-step spectrum denoising method.

Yao Xie^1,2, Rencheng Song³, Dong Yang²

¹School of Engineering Science, University of Science and Technology of China, Hefei, 230027, People's Republic of China.

Physiological Measurement

|August 25, 2021

Summary

This summary is machine-generated.

This study introduces a novel two-step method to remove motion artifacts from impedance cardiography (ICG) signals, improving the accuracy of cardiac output and stroke volume measurements during rest and exercise.

Keywords:

canonical correlation analysis (CCA)ensemble empirical mode decomposition (EEMD)impedance cardiography (ICG)motion artifacts spectral subtraction

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Area of Science:

Biomedical Engineering
Cardiovascular Physiology
Signal Processing

Background:

Impedance cardiography (ICG) offers noninvasive, continuous monitoring of stroke volume and cardiac output.
ICG measurements are susceptible to artifacts from respiration and body movement, limiting clinical utility.
Existing methods struggle to effectively remove motion-induced noise in ICG signals.

Purpose of the Study:

To develop and validate a two-step spectrum denoising method for motion artifact removal in ICG.
To enhance the reliability of ICG measurements during both resting and exercising conditions.
To improve the diagnostic accuracy of cardiovascular assessments using ICG.

Main Methods:

A two-step spectral denoising approach combining spectral subtraction and ensemble empirical mode decomposition (EEMD).
Utilized simultaneous electrocardiogram (ECG), ICG, and acceleration signals for artifact identification and removal.
Employed canonical correlation analysis (CCA) in the spectral domain to extract shared information between ICG and ECG intrinsic mode functions (IMFs).

Main Results:

The denoising method significantly reduced motion artifacts in ICG signals across 30 subjects.
The beat contribution factor of ICG signals improved from 80.1% to 97.4% after artifact removal.
Effective artifact suppression was demonstrated in both resting and dynamic (cycling) scenarios.

Conclusions:

The proposed spectral denoising scheme effectively enhances ICG signal quality by removing motion artifacts.
Improved ICG reliability supports more accurate cardiovascular disease diagnosis and analysis.
This method offers a promising solution for robust, noninvasive hemodynamic monitoring.