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Ensemble empirical mode decomposition for high frequency ECG noise reduction.

Kang-Ming Chang¹

¹Department of Optoelectronic and Communication Engineering, Asia University, Taichung County, Taiwan. changkm@asia.edu.tw

Biomedizinische Technik. Biomedical Engineering

|June 24, 2010

Summary

Ensemble empirical mode decomposition (EEMD) effectively reduces noise in electrocardiogram (ECG) signals. This method outperforms traditional empirical mode decomposition (EMD) and infinite impulse response (IIR) filters in reconstructing clean ECG data.

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Area of Science:

Biomedical Engineering
Signal Processing

Background:

Electrocardiogram (ECG) signals are susceptible to noise from various sources, impacting diagnostic accuracy.
Empirical Mode Decomposition (EMD) is a signal processing technique used for nonlinear and non-stationary data analysis, including ECG noise reduction.

Purpose of the Study:

To evaluate the effectiveness of Ensemble Empirical Mode Decomposition (EEMD) for reducing high-frequency noise in ECG signals.
To compare the performance of EEMD against traditional EMD and Infinite Impulse Response (IIR) filters for ECG noise reduction.

Main Methods:

EEMD was applied to ECG signals corrupted by muscle contraction, power line interference, and simulated Gaussian noise.
Intrinsic Mode Functions (IMFs) were decomposed, with noise-containing IMFs removed before signal reconstruction.
Mean Square Error (MSE) was used to quantify the reconstruction performance.

Main Results:

EEMD successfully reduced noise, with the first one or two IMFs typically containing noise components and being discarded.
The reconstructed ECG signals using EEMD showed lower MSE compared to EMD and IIR filter methods.
EEMD demonstrated a reduced mode-mixing effect, leading to improved separation of signal components.

Conclusions:

EEMD offers a superior approach to ECG noise reduction compared to conventional EMD and IIR filtering techniques.
The ensemble averaging in EEMD effectively mitigates noise and improves the fidelity of the reconstructed ECG signal.