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Noise reduction in magnetocardiography by singular value decomposition and independent component analysis

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Abstract

In the routine recording of magnetocardiograms (MCGs), it is necessary to underline the problem of noise cancellation. Source separation has often been suggested to solve this problem. In this paper, blind source separation (BSS), by means of singular value decomposition (SVD) and independent component analysis (ICA), was used for noise reduction in MCG data to improve the signal to noise ratio. Special techniques, based on statistical parameters, for identifying noise and disturbances, have been introduced to automatically eliminate noise-related and disturbance-related components before reconstructing cleaned data sets. The results show that ICA and SVD can detect and remove a variety of noise and artefact sources from MCG data, as well as from stress MCG.

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Authors and Affiliations

  1. BMDSys, Biomagnetische DiagnoseSysteme GmbH, Wildenbruchstr. 15, 07745, Jena, Germany

    D. DiPietroPaolo

  2. HPM - Physic and Software, Neu-Ulm, Germany

    H.-P. Müller

  3. Fraunhofer FIRST.IDA, Berlin, Germany

    G. Nolte

  4. Division for Biosignals and Imaging Technologies, Neurology Klinik, FS University Jena, Jena, Germany

    S. N. Erné

Authors
  1. D. DiPietroPaolo
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  2. H.-P. Müller
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  3. G. Nolte
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  4. S. N. Erné
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Correspondence to D. DiPietroPaolo.

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DiPietroPaolo, D., Müller, HP., Nolte, G. et al. Noise reduction in magnetocardiography by singular value decomposition and independent component analysis. Med Bio Eng Comput 44, 489–499 (2006). https://doi.org/10.1007/s11517-006-0055-z

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  • DOI: https://doi.org/10.1007/s11517-006-0055-z

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