Abstract
In this paper, the performance of Empirical Mode Decomposition (EMD) applied in biomedical signals is investigated and especially it is considered the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with White Gaussian Noise (WGN) as well as real ECG records are employed and a variety of time series lengths is processed with EMD in order to extract the Intrinsic Mode Functions (IMF). Computation time is measured upon the completion of the process in simulation campaign stage and real records stage and the results are compared in both cases. Spectral characteristics of the time series as well as the tendency to exhibit extrema are the key factors with significant impact on both computation time as well as the total number of IMFs produced.
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