Magnetocardiogram Topography with Automatic Artifact Correction using Principal Component Analysis and Artificial Neural Network

  • Ahn C.B. (VIA Multimedia Center, Kwangwoon University) ;
  • Kim T.H. (VIA Multimedia Center, Kwangwoon University) ;
  • Park H.C. (VIA Multimedia Center, Kwangwoon University) ;
  • Oh S.J. (VIA Multimedia Center, Kwangwoon University)
  • 발행 : 2006.04.01

초록

Magnetocardiogram (MCG) topography is a useful diagnostic technique that employs multi-channel magnetocardiograms. Measurement of artifact-free MCG signals is essenctial to obtain MCG topography or map for a diagnosis of human heart. Principal component analysis (PCA) combined with an artificial neural network (ANN) is proposed to remove a pulse-type artifact in the MCG signals. The algorithm is composed of a PCA module which decomposes the obtained signal into its principal components, followed by an ANN module for the classification of the components automatically. In the experiments with volunteer subjects, 97% of the decisions that were made by the ANN were identical to those by the human experts. Using the proposed technique, the MCG topography was successfully obtained without the artifact.

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