Abstract
Band-pass, Kalman, and adaptive filters are used for removal of resuscitation artifacts from human ECG signals. The paper is tutorial and clarifies the rationale for applying these methods in the particular biomedical context. Novel aspects of the exposition are deterministic interpretation and comparative study of the methods. A database of separately recorded human ECG and animal resuscitation artifact signals is used for evaluation of the methods. The considered performance criterion is the signal-to-noise ratio (SNR) improvement, defined as the ratio of the SNRs of the filtered signal and the given ECG signal y. The empirical results show that for low SNR of y a band-pass filter yields the best performance while for high SNR an adaptive filter yields the best performance.
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Notes
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Note, that the persistency of excitation assumption ensures that U(t)U ⊤(t) is invertible. Therefore, ill-conditioning corresponds to input signals that are almost not persistently exciting.
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We would like to thank Simon Doclo from K.U. Leuven for a Matlab code for LMS adaptive filtering.
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Acknowledgements
The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant agreement number 258581 “Structured low-rank approximation: Theory, algorithms, and applications”; the Austrian Fonds zur Förderung der Wissenschaftlichen Forschung (FWF, Vienna, grant No. L288), Research Council KUL: GOA-AMBioRICS, GOA-Mefisto 666, Center of Excellence EF/05/006 “Optimization in engineering”, several PhD/postdoc & fellow grants; Flemish Government: FWO: PhD/postdoc grants, projects, G.0360.05 (EEG signal processing), G.0321.06 (numerical tensor techniques), research communities (ICCoS, ANMMM); IWT: PhD Grants; Belgian Federal Science Policy Office IUAP P5/22 (‘Dynamical Systems and Control: Computation, Identification and Modelling’); EU: BIOPATTERN, ETUMOUR; HEALTHagents.
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Markovsky, I., Amann, A., Van Huffel, S. (2012). Application of Filtering Methods for Removal of Resuscitation Artifacts from Human ECG Signals. In: Wang, L., Garnier, H. (eds) System Identification, Environmental Modelling, and Control System Design. Springer, London. https://doi.org/10.1007/978-0-85729-974-1_14
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