Abstract
In this paper we propose a modification of the clustering based nonlinear state–space projection (CNPF) method. The whole filtering process takes place in a reconstructed state–space by applying the time–delay embedding technique. Then, each state-space point is projected on the constructed signal subspace. The subspace has a constant dimension and is the same for all constructed subspaces. A modification is introduced in such a way that subspace dimension may differ depending on the currently processed part of the signal. For this purpose we introduced a concept of signal’s eigenportrait.
The proposed method is applied to process different ECG signals contaminated with a real electromyographic (EMG) noise. As the reference, the CNPF method is used. For different noise levels, the proposed method appears more effective than the reference one.
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Acknowledgement
This research was supported by statutory funds (BK-2017, BK-2018) of the Institute of Electronics, Silesian University of Technology. The work was performed using the infrastructure supported by POIG.02.03.01-24-099/13 grant: GeCONiI–Upper Silesian Center for Computational Science and Engineering.
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Przybyła, T., Pander, T. (2021). Projective Filtering with Adaptive Selected Projective Dimensions. In: Atanassov, K., et al. Uncertainty and Imprecision in Decision Making and Decision Support: New Challenges, Solutions and Perspectives. IWIFSGN 2018. Advances in Intelligent Systems and Computing, vol 1081. Springer, Cham. https://doi.org/10.1007/978-3-030-47024-1_26
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DOI: https://doi.org/10.1007/978-3-030-47024-1_26
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