Confocal microscopy-based estimation of intracellular conductivities in myocardium for modeling of the normal and infarcted heart.

Ventricular arrhythmias are the leading cause of mortality in patients with ischemic heart diseases, such as myocardial infarction (MI). Computational simulation of cardiac electrophysiology provides insights into these arrhythmias and their treatment. However, only sparse information is available on crucial model parameters, for instance, the anisotropic intracellular electrical conductivities.

Etiology-Specific Remodeling in Ventricular Tissue of Heart Failure Patients and Its Implications for Computational Modeling of Electrical Conduction.

With an estimated 64.3 million cases worldwide, heart failure (HF) imposes an enormous burden on healthcare systems. Sudden death from arrhythmia is the major cause of mortality in HF patients.

Extended Bidomain Modeling of Defibrillation: Quantifying Virtual Electrode Strengths in Fibrotic Myocardium.

Defibrillation is a well-established therapy for atrial and ventricular arrhythmia. Here, we shed light on defibrillation in the fibrotic heart. Using the extended bidomain model of electrical conduction in cardiac tissue, we assessed the influence of fibrosis on the strength of virtual electrodes caused by extracellular electrical current.

A Matched-Filter-Based Algorithm for Subcellular Classification of T-System in Cardiac Tissues.

In mammalian ventricular cardiomyocytes, invaginations of the surface membrane form the transverse tubular system (T-system), which consists of transverse tubules (TTs) that align with sarcomeres and Z-lines as well as longitudinal tubules (LTs) that are present between Z-lines in some species. In many cardiac disease etiologies, the T-system is perturbed, which is believed to promote spatially heterogeneous, dyssynchronous Ca release and inefficient contraction. In general, T-system characterization approaches have been directed primarily at isolated cells and do not detect subcellular T-system heterogeneity.

Confocal Microscopy-Based Estimation of Parameters for Computational Modeling of Electrical Conduction in the Normal and Infarcted Heart.

Computational modeling is an important tool to advance our knowledge on cardiac diseases and their underlying mechanisms. Computational models of conduction in cardiac tissues require identification of parameters. Our knowledge on these parameters is limited, especially for diseased tissues.