Cardiac biomagnetic source estimation with a heart-torso model and a trained neural network.
The intensity of the cardiac sources for normal adult subjects was estimated from given magnetic field profiles with a trained neural network based on the relationship of the electrical activity of the heart to the cardiac magnetic fields. The input for training the neural network consisted of the magnetic field profiles above the torso during the heartbeat. The outputs were the dipole intensities which produced those magnetic field profiles. A back propagating algorithm with bias and momentum was utilized for training. The measured and simulated torso magnetic field profiles and magnetocardiograms were used for training the neural network. Estimation of the dipole intensities was performed for unknown magnetic field profiles with the trained neural network. The estimated cardiac dipole intensities were reasonably close to the true dipole intensities. These results show the feasibility of the estimation of cardiac dipole intensities with a trained neural network under a very restricted forward model of the cardiac magnetic fields. Generalization of the results to cover a large population base could be difficult because the activation isochrones are different from subject to subject.
Ramon
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Department of Electrical Engineering, University of Washington, Seattle 98195, USA. ceon@u.washington.edu
Casem
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D000328:Adult; D003198:Computer Simulation; D006321:Heart / Q000502:physiology* / Q000528:radiation effects; D006329:Heart Conduction System / Q000502:physiology*; D006334:Heart Function Tests; D006801:Humans; D008280:Magnetics; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology