Powerline interference reduction in ECG signals using empirical wavelet transform and adaptive filtering.

Area of Science:

• Biomedical Signal Processing
• Digital Signal Processing
• Medical Informatics

Background:

• Electrocardiogram (ECG) signals are vital for cardiac diagnostics but susceptible to noise.
• Common noise sources include powerline interference (PLI), baseline wander, and muscle artifacts.
• Effective noise reduction is crucial for accurate clinical interpretation of ECG data.

Purpose of the Study:

• To develop and evaluate novel methods for reducing powerline interference (PLI) in ECG signals.
• To compare the performance of empirical wavelet transform (EWT) based methods against empirical mode decomposition (EMD) based methods for PLI cancellation.
• To assess the efficiency and computational complexity of the proposed de-noising techniques.

Main Methods:

• Implementation of novel PLI reduction techniques utilizing empirical wavelet transform (EWT).
• Comparative analysis of six different PLI reduction methods, incorporating both EWT and EMD.
• Application of adaptive filtering in conjunction with EWT for noise cancellation.

Main Results:

• EWT-based de-noising methods demonstrate superior performance in PLI reduction for ECG signals.
• The proposed EWT methods exhibit significantly less computational complexity compared to EMD-based approaches.
• EWT methods provide a more efficient solution for removing powerline interference from clinical ECG recordings.

Conclusions:

• Empirical wavelet transform (EWT) offers an effective and efficient approach for powerline interference reduction in ECG signals.
• EWT-based de-noising techniques present a viable alternative to traditional EMD methods, with enhanced performance and reduced computational load.
• The developed methods contribute to improving the quality of ECG signal acquisition in clinical settings.