A fast algorithm for spatiotemporal signals recovery using arbitrary dictionaries with application to electrocardiographic imaging. (1st November 2022)

• Record Type: Journal Article
• Authors: Caracciolo, S F; Caiafa, C F; Martínez Pería, F D; Arini, P D
• Is Part Of: Biomedical physics & engineering express. Volume 8:Number 6(2022)
• Publication Date: 2022-11-01
• Subjects: ECGI -- group lasso -- Kronecker product -- sparse regularization; Medical physics -- Periodicals; Biophysics -- Periodicals; Biomedical engineering -- Periodicals; Medical sciences -- Periodicals; 610.153
• DOI: 10.1088/2057-1976/ac835b
• Languages: English
• ISSNs: 2057-1976

Abstract

Abstract: This paper presents a method to solve a linear regression problem subject to group lasso 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.