Why the study?
To solve the inverse problem of electrocardiography using sparse signal representation over a redundant dictionary or frame.
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The developed algorithm reduces space and temporal complexity for solving the inverse problem of electrocardiography by avoiding explicit computation of the underlying Kronecker structure.
and ridge penalisation when the model has a Kronecker structure. This model was developed to solve the inverse problem of electrocardiography using sparse signal representation over a redundant dictionary or frame. The optimisation algorithm was performed using the block coordinate descent and proximal gradient descent methods. The explicit computation of the underlying Kronecker structure in the regression was avoided, reducing space and temporal complexity. We developed an algorithm that supports the use of arbitrary dictionaries to obtain solutions and allows a flexible group distribution.
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Caracciolo et al. (2022) studied this question.
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