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

D DiPietroPaolo, H P Müller, G Nolte, S N Erné

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

Journal Article: Medical & Biological Engineering & Computing (impact factor: 1.76). 07/2006; 44(6):489-99. DOI: 10.1007/s11517-006-0055-z

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.

Source: PubMed

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Keywords

artefact sources
 
blind source separation
 
BSS
 
disturbance-related components
 
independent component analysis
 
MCG data
 
noise reduction
 
noise-related
 
reconstructing cleaned data sets
 
routine recording
 
singular value decomposition
 
Source separation
 
Special techniques
 
stress MCG