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R-peak detection and signal averaging for simulated stress ECG using EMD.

Amit J Nimunkar¹, Willis J Tompkins

¹Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 16, 2007

Summary

Empirical mode decomposition (EMD) effectively detects R-peaks in noisy electrocardiogram (ECG) signals, outperforming traditional methods. This EMD-based approach enhances stress ECG analysis by improving signal quality and R-peak accuracy.

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Area of Science:

Biomedical Engineering
Signal Processing
Cardiology

Background:

Electromyogram (EMG) noise significantly degrades electrocardiogram (ECG) signal quality.
Accurate R-peak detection is crucial for diagnosing cardiac conditions, especially during stress tests.

Purpose of the Study:

To evaluate the efficacy of Empirical Mode Decomposition (EMD) for R-peak detection in ECG signals corrupted by EMG-like noise.
To compare EMD-based filtering and signal averaging with conventional techniques for stress ECG enhancement.

Main Methods:

Modeled EMG noise as white Gaussian noise with signal-to-noise ratios (SNRs) between -10 dB and -20 dB.
Applied EMD for R-peak detection and signal filtering.
Compared EMD performance against the Pan-Tompkins algorithm and traditional low-pass filtering.
Utilized signal averaging with EMD-processed data and compared it to standard signal averaging.

Main Results:

EMD-based R-peak detection achieved results comparable to the established Pan-Tompkins algorithm.
EMD filtering demonstrated effectiveness in noise reduction for ECG signals.
EMD-based signal averaging showed improvements over standard techniques for stress ECG.

Conclusions:

EMD is a promising technique for robust R-peak detection in noisy ECG signals.
EMD-based filtering and signal averaging enhance the quality of stress ECG recordings.
This approach holds potential for improved diagnosis and monitoring of cardiac stress.