Power-line interference and baseline wander elimination in ECG using VMD and EWT

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Electrocardiogram (ECG) signals are vital for diagnosing cardiovascular disorders.
• Clinical ECG acquisition is often contaminated by noise like powerline interference (PLI) and baseline wandering (BLW).
• Signal distortion from noise can lead to misinterpretation and inaccurate diagnoses.

Purpose of the Study:

• To develop and evaluate a novel hybrid ECG denoising method.
• To improve the reliability of ECG signal analysis for better cardiovascular diagnosis.
• To address the challenge of noise reduction in clinical ECG data.

Main Methods:

• A hybrid approach combining Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT).
• Noisy ECG signals are decomposed into narrow-band variational mode functions (VMFs).
• Adaptive wavelet filter banks are designed using EWT based on VMF center frequencies for noise removal.

Main Results:

• The proposed method was applied to ECG signals from the MIT-BIH Arrhythmia database.
• Performance was evaluated using the MIT-BIH Noise Stress Test Database (NSTDB) with key metrics: Percentage-Root-Mean-Square Difference (PRD) and Signal-to-Noise Ratio (SNR).
• The hybrid VMD-EWT method achieved a lower PRD and higher SNR compared to existing denoising techniques, with an average SNR of 24.03 and a 5% PRD reduction.

Conclusions:

• The novel hybrid VMD-EWT method demonstrates superior performance in ECG denoising.
• This technique offers a significant improvement for reliable ECG signal analysis and cardiovascular diagnosis.
• The approach effectively removes noise components, enhancing the quality of biomedical signals.