Solving the Inverse Problem of Electrocardiography in a Realistic Environment Using a Spatio-Temporal LSQR-Tikhonov Hybrid Regularization Method

The purpose of the inverse electrocardiographic problem is to reconstruct the electrophysiological activities in the human heart from electrical signals measured on the body surface. This is a promising noninvasive approach to obtain an insight into cardiac diseases. However, this problem is illposed and regularization is required to stabilize the inverse solution. In the present work a spatio-temporal LSQR-Tikhonov hybrid regularization method is proposed, which combines the spatio-temporal and hybrid regularization frameworks. The novel method is tested in a realistic environment considering measurement noise, the modeling error induced by neglecting heart motion and baseline wander in ECG. The spatio-temporal hybrid regularization method achieves more stable results in the realistic environment compared to the Tikhonov regularization and the spatial LSQR-Tikhonov regularization. Moreover, the computation time is dramatically reduced thanks to the Greensite’s spatio-temporal approach.

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