Restoration of Magnetocardiography Signal Using Principal Component Analysis and Artificaial Neural Network

Magnetocardiogram (MCG) is a biomagnetic field that is produced by cardiac electrical activity. The MCG signal can be measured with the use of a superconducting quantum interference device (SQUID). A two-dimensional map, MCG topography, can be obtained from multi-channel MCG sensors. In this paper, 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. After classification, the signal was restored through the reconstruction of the components that belong to the signal class. Using the proposed technique, the artifact was successfully removed from the MCG signal.