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Reconstruction of Fetal Cardiac Vectors From Multichannel fMCG Data Using Recursively Applied and Projected Multiple Signal Classification | IEEE Journals & Magazine | IEEE Xplore

Reconstruction of Fetal Cardiac Vectors From Multichannel fMCG Data Using Recursively Applied and Projected Multiple Signal Classification


Abstract:

Previous attempts at unequivocal specification of signal strength in fetal magnetocardiographic (fMCG) recordings have used an equivalent current dipole (ECD) to estimate...Show More

Abstract:

Previous attempts at unequivocal specification of signal strength in fetal magnetocardiographic (fMCG) recordings have used an equivalent current dipole (ECD) to estimate the cardiac vector at the peak of the averaged QRS complex. However, even though the magnitude of fetal cardiac currents are anticipated to be relatively stable, ECD-based estimates of signal strength show substantial and unrealistic variation when comparing results from different time windows of the same recording session. The present study highlights the limitations of the ECD model, and proposes a new methodology for fetal cardiac source reconstruction. The proposed strategy relies on recursive subspace projections to estimate multiple dipoles that account for the distributed myocardial currents. The dipoles are reconstructed from spatio-temporal fMCG data, and are subsequently used to derive estimators of the cardiac vector over the entire QRS. The new method is evaluated with respect to simulated data derived from a model of ventricular depolarization, which was designed to account for the complexity of the fetal cardiac source configuration on the QRS interval. The results show that the present methodology overcomes the drawbacks of conventional ECD fitting, by providing robust estimators of the cardiac vector. Additional evaluation with real fMCG data show fetal cardiac vectors whose morphology closely resembles that obtained in adult MCG
Published in: IEEE Transactions on Biomedical Engineering ( Volume: 53, Issue: 12, December 2006)
Page(s): 2564 - 2576
Date of Publication: 20 November 2006

ISSN Information:

PubMed ID: 17153214

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