Key result
A novel algorithm based on low rank and sparse decomposition combined with total variation improved the accuracy of cardiac potential recovery in solving the dynamic ECG-inverse problem.
Why the study?
Most calculation methods for the ECG inverse problem rely on priori assumptions of cardiac electrophysiology instantaneous characteristics.
Does an algorithm based on LSD + TV improve the accuracy of cardiac potential recovery in the ECG inverse problem?
Does an algorithm based on LSD + TV improve the accuracy of cardiac potential recovery in the ECG inverse problem?
A novel LSD + TV algorithm improves the accuracy of solving the dynamic ECG-inverse problem for locating premature ventricular contraction origins.
May aid noninvasive PVC localization; leaves open clinical validation before practice change.
Most of the calculation methods for the electrocardiograph (ECG) inverse problem are based on priori assumptions of the instantaneous characteristics of the cardiac electrophysiology. In this paper, we have proposed a novel algorithm based on low rank and sparse decomposition (LSD) + total variation (TV) to solve the illposedness of dynamic ECG-inverse problem. The TV constraint filters out the disturbance of the noise and maintains the local smoothness of the potential. The LSD separates the sparse details from the potential background to prevent the potential details from being lost under the effect of smoothing constraint, thereby improving the accuracy of cardiac potential recovery.
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Fang et al. (2019) studied Premature Ventricular Contraction. Low rank and sparse decomposition (LSD) + total variation (TV) algorithm was evaluated on Accuracy of cardiac potential recovery. A novel algorithm based on low rank and sparse decomposition combined with total variation improved the accuracy of cardiac potential recovery in solving the dynamic ECG-inverse problem.
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