Abstract
In this paper an efficient filtering procedure based on the Singular Value Decomposition (SVD) has been proposed. SVD, a high resolution spectrum estimation tools, is used to decompose the ECG data matrix into orthogonal subspaces. Due to the energy-preserving orthogonal transformation in the SVD, these subspaces correspond to the signal and noise components contained in the ECG data. Projection of the data onto the desired subspace eliminates the noise and the unwanted signal components.
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