Research on Denoising Methods for Magnetocardiography Signals in a Non-Magnetic Shielding Environment.

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Signal Processing

Background:

• Magnetocardiography (MCG) measures cardiac electrical activity via weak magnetic fields for noninvasive cardiovascular disease screening.
• External magnetic interference and physiological artifacts severely degrade MCG signal quality in unshielded environments.

Purpose of the Study:

• To develop and validate an integrated denoising framework (AOA-VMD-WT) for enhancing MCG signal quality in clinical settings.
• To adaptively optimize Variational Mode Decomposition (VMD) parameters using the Arithmetic Optimization Algorithm (AOA) for improved denoising efficacy.

Main Methods:

• The AOA-VMD-WT framework adaptively optimizes VMD parameters (K, α) using AOA.
• Signal components are denoised based on modal center frequencies: high-frequency suppression (≥50 Hz), wavelet thresholding (<50 Hz), and baseline correction (<0.5 Hz).
• Quantitative evaluation using QRS amplitude retention, frequency suppression, and spectral entropy compared against FIR and wavelet methods.

Main Results:

• The AOA-VMD-WT method demonstrated significant improvements: 8-15 dB higher frequency suppression, 2-8 dB lower frequency suppression, and a 0.1-0.6 decrease in spectral entropy.
• The framework maintained QRS amplitude integrity.
• Parameter optimization via AOA showed high stability across experiments.

Conclusions:

• The proposed AOA-VMD-WT framework offers robust and stable MCG signal denoising in typical clinical environments.
• This algorithmic approach supports reliable MCG measurements for early cardiovascular disease detection.
• The integrated denoising strategy effectively mitigates various noise sources impacting MCG signals.