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

This study used empirical mode decomposition (EMD) for R-peak detection in electrocardiogram signals in the presence of electromyogram-like noise. The EMG was modeled as random white Gaussian noise with a signal-to-noise ratio (SNR) in the range of around -10 dB to -20 dB. The EMD-based R-peak detection technique gives results comparable to those obtained with the Pan-Tompkins algorithm. The EMD technique is implemented for filtering of noisy ECG signals and is further compared with a traditional low-pass filtering approach. Finally signal averaging is performed using the EMD-based R-peak detection and filtering approach and compared with the standard signal averaging technique. We conclude that the EMD based technique for R-peak detection and filtering shows promise for enhancement of the stress ECG.