Arrhythmia ECG noise reduction by ensemble empirical mode decomposition

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Electrocardiogram (ECG) signals are susceptible to various noise artifacts.
• Artifacts can obscure crucial diagnostic information in ECG traces.
• Effective noise reduction is vital for accurate ECG analysis.

Purpose of the Study:

• To propose a novel noise filtering algorithm for ECG artifact removal.
• To evaluate the performance of an ensemble empirical mode decomposition (EEMD) based filter.
• To compare EEMD filtering with traditional and empirical mode decomposition (EMD) methods.

Main Methods:

• Simulated and real ECG signals were corrupted with 50 Hz, EMG, and baseline wander noise.
• Noise filtering was performed using IIR filter, Wiener filter, EMD, and EEMD.
• Filtering performance was quantified using mean square error (MSE) between clean and filtered ECGs.

Main Results:

• The EEMD-based filter demonstrated significant noise reduction capabilities.
• High noise reduction was observed across different noise patterns and signal types.
• The filter showed particular effectiveness in reducing noise in arrhythmia ECGs.

Conclusions:

• Ensemble empirical mode decomposition (EEMD) offers a robust approach for ECG noise filtering.
• The proposed EEMD-based algorithm provides superior artifact removal compared to traditional methods.
• This technique enhances the quality of ECG signals, aiding in more reliable diagnosis.