Abstract
Regularization is a key step for solving the ill-posed inverse problem of electrocardiography (ECG). In this paper, a novel regularization technique (LSQR-Tik) which combines the least square QR (LSQR) method with a Tikhonov-like prior information term is proposed. This technique needs to select two parameters, the Tikhonov-like regularization parameter (λ) and the iteration number of LSQR-Tik (k), which can be determined by a modified L-curve technique. The performance of the LSQR-Tik method for solving the inverse ECG problem was evaluated based on a realistic heart-torso model simulation protocol. The results show that the LSQR-Tik method could overcome the ill-pose property effectively and get better inverse solutions than those of Tikhonov and LSQR methods, especially in the case of body surface potential with large noises.
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Jiang, M., Xia, L., Shou, G. (2007). Combining Regularization Frameworks for Solving the Electrocardiography Inverse Problem. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_136
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DOI: https://doi.org/10.1007/978-3-540-74282-1_136
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