ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiovascular Technology

Background:

• Electrocardiogram (ECG) signals are crucial for diagnosing heart conditions.
• Noisy ECG data and baseline wander can impede accurate analysis.
• Existing de-noising methods may suffer from issues like mode-mixing.

Purpose of the Study:

• To propose a novel method for de-noising ECG signals and correcting baseline wander.
• To address the limitations of traditional Empirical Mode Decomposition (EMD) methods.
• To leverage Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for ECG signal processing.

Main Methods:

• Decomposition of noisy ECG signals into Intrinsic Mode Functions (IMFs) using CEEMDAN.
• Identification and de-noising of noise-corrupted IMFs via wavelet thresholding.
• Removal of IMFs with low Zero-Crossing Rate (ZCR) and signal reconstruction.

Main Results:

• Effective suppression of random noise in ECG signals was demonstrated.
• Efficient correction of baseline wander was achieved.
• Validation using the MIT-BIH ECG database confirmed the method's efficacy.

Conclusions:

• The proposed CEEMDAN and wavelet thresholding method offers a robust solution for ECG signal enhancement.
• This technique improves the quality of ECG signals for better diagnostic accuracy.
• It provides a valuable tool for researchers and clinicians working with ECG data.