Sparse Representation for Cardiac Electrical Activity Imaging in Magnetocardiography

Abstract

Signal sparsity has been widely discussed in communication system, cloud computing, multimedia processing and computational biology. Reconstructing the sparsely distributed current sources of the heart by means of non-invasive magnetocardiography (MCG) measurement and various optimization methods provides a new way to solve the inverse problem of the cardiac magnetic field. The problem of sparse source location of MCG is in the time series of MCG measurement caused by active sparse current source, can the spatiotemporal source be reconstructed accurately and effectively? For the above problem, the scientific contributions of the paper include: (1) A modified focal underdetermined system solver algorithm is proposed for a sparse solution, by combing with dynamic regularization factor and smoothed sparse constraint; (2) Lead field matrix is reduced by prior information of cardiac magnetic field map to reduce under-determination; (3) Spatiotemporal sources are reconstructed for non-invasive cardiac electrical activity imaging. The results of real MCG data demonstrate the effectiveness of this method for cardiac electrical activity imaging. The temporal and spatial changes of the current sources are similar to the depolarization and repolarization process of the ventricle.