A Novel ECG Signal Denoising Algorithm Based on Sparrow Search Algorithm for Optimal Variational Modal Decomposition

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiovascular Disease Diagnosis

Background:

• Electrocardiogram (ECG) signal processing is vital for cardiovascular disease prevention and diagnosis.
• ECG signals are prone to noise interference from equipment, environment, and transmission.

Purpose of the Study:

• To propose an efficient denoising method for ECG signals.
• To address noise interference and baseline drift in ECG signals.

Main Methods:

• A novel VMD-SSA-SVD algorithm was developed, combining Variational Modal Decomposition (VMD), Sparrow Search Algorithm (SSA), and Singular Value Decomposition (SVD).
• SSA optimized VMD parameters [K,α] for signal decomposition.
• Baseline drift components were eliminated, and effective modes were processed using SVD for noise reduction and signal reconstruction.

Main Results:

• The VMD-SSA-SVD algorithm demonstrated superior noise reduction compared to Wavelet Packet Decomposition, EMD, EEMD, and CEEMDAN.
• The method effectively suppressed noise and removed baseline drift.
• Key morphological characteristics of the ECG signals were preserved.

Conclusions:

• The VMD-SSA-SVD algorithm offers a significant advancement in ECG signal denoising.
• This technique enhances the reliability of ECG analysis for cardiovascular disease diagnosis.
• The method provides a robust solution for cleaning noisy ECG data.