Data Partitioning Based Weighted Averaging for Noise Suppression in Biomedical Signals

In the case of biomedical signals with a quasi-cyclic character, such as electrocardiographic signals, the high resolution electrocardiograms or electrical potentials recorded from the nervous system of patients (estimating brain activity evoked by a known stimulus), as a method of averaging in the time domain may be used for noise attenuation. In this paper there is presented input data partitioning applied to a few different methods of weighted averaging. This procedure usually leads to improve the quality of the resulting averaged signal, especially when fuzzy partitioning is used. Below it is presented the computational study of weighted averaging with traditional (sharp) and fuzzy partition of the input data in the presence of non-stationary noise. The performance of presented methods is experimentally evaluated for analytical signal of EN 60601-2-51 (2003), namely ANE20000 ECG record.