Information theory to tachycardia therapy: electrogram entropy predicts diastolic microstructure of reentrant ventricular tachycardia.

There is no known strategy to differentiate which multicomponent electrograms in sinus rhythm maintain reentrant ventricular tachycardia (VT). Low entropy in the voltage breakdown of a multicomponent electrogram can localize conditions suitable for reentry but has not been validated against the classic VT activation mapping. We examined whether low entropy in a late and diversely activated ventricular scar region characterizes and differentiates the diastolic path of VT and represents protected tissue channels devoid of side branches.

Determinants of atrial bipolar voltage: Inter electrode distance and wavefront angle.

Background: Local bipolar electrogram (EGM) peak-to-peak voltage (Vpp) is currently used to characterise mapped myocardial substrate. However, how interelectrode distance and angle of wavefront incidence affect bipolar, Vpp values, in the current era of multi-electrode mapping is unknown.

Objectives: To elucidate the effects of tissue and electrode geometry on bipolar Vpp measurements, when mapping healthy versus diseased atrial regions.

Quantifying the determinants of decremental response in critical ventricular tachycardia substrate.

Background: Decremental response evoked with extrastimulation (DEEP) is a useful tool for determining diastolic return path of ventricular tachycardia (VT). Though a targeted VT ablation is feasible with this approach, determinants of DEEP response have not been studied OBJECTIVES: To elucidate the effects of clinically relevant factors, specifically, the proximity of the stimulation site to the arrhythmogenic scar, stimulation wave direction, number of channels open in the scar, size of the scar and number of extra stimuli on decrement and entropy of DEEP potentials.

Methods: In a 3-dimensional bi-domain simulation of human ventricular tissue (TNNP cell model), an irregular subendocardial myopathic region was generated.

Electrophysiological Cardiac Modeling: A Review.

Cardiac electrophysiological modeling in conjunction with experimental and clinical findings has contributed to better understanding of electrophysiological phenomena in various species. As our knowledge on underlying electrical, mechanical, and chemical processes has improved over time, mathematical models of the cardiac electrophysiology have become more realistic and detailed. These models have provided a testbed for various hypotheses and conditions that may not be easy to implement experimentally.

Feature-based MRI data fusion for cardiac arrhythmia studies.

Current practices in studying cardiac arrhythmias primarily use electrical or optical surface recordings of a heart, spatially limited transmural recordings, and mathematical models. However, given that such arrhythmias occur on a 3D myocardial tissue, information obtained from such practices lack in dimension, completeness, and are sometimes prone to oversimplification. The combination of complementary Magnetic-Resonance Imaging (MRI)-based techniques such as Current Density Imaging (CDI) and Diffusion Tensor Imaging (DTI) could provide more depth to current practices in assessing the cardiac arrhythmia dynamics in entire cross sections of myocardium.

Noise distribution and denoising of current density images.

Current density imaging (CDI) is a magnetic resonance (MR) imaging technique that could be used to study current pathways inside the tissue. The current distribution is measured indirectly as phase changes. The inherent noise in the MR imaging technique degrades the accuracy of phase measurements leading to imprecise current variations.