Cardiac fibrosis and other scarring of the heart, arising from conditions ranging from myocardial infarction to ageing, promotes dangerous arrhythmias by blocking the healthy propagation of cardiac excitation. Owing to the complexity of the dynamics of electrical signalling in the heart, however, the connection between different arrangements of blockage and various arrhythmic consequences remains poorly understood. Where a mechanism defies traditional understanding, machine learning can be invaluable for enabling accurate prediction of quantities of interest (measures of arrhythmic risk) in terms of predictor variables (such as the arrangement or pattern of obstructive scarring). In this study, we simulate the propagation of the action potential (AP) in tissue affected by fibrotic changes and hence detect sites that initiate re-entrant activation patterns. By separately considering multiple different stimulus regimes, we directly observe and quantify the sensitivity of re-entry formation to activation sequence in the fibrotic region. Then, by extracting the fibrotic structures around locations that both do and do not initiate re-entries, we use neural networks to determine to what extent re-entry initiation is predictable, and over what spatial scale conduction heterogeneities appear to act to produce this effect. We find that structural information within about 0.5 mm of a given point is sufficient to predict structures that initiate re-entry with more than 90% accuracy.

No abstract

The main aim of this paper is to study the evolution of the transmembrane potential on the cardiac cell under different rates and amplitudes of stimulation. For modeling this potential, the modification of the Fenton‐Karma model was applied. It is a phenomenological model with 3 degrees of freedom that corresponds to nondimensional transmembrane potential and gating variables for regulation of inward and outward ion currents that can better reproduce the shape of the transmembrane potential than the original Fenton‐Karma model. The model was newly forced by stimulus with the shape of the half‐sine period. As the main goal of the paper is to show that this model is showing regular as well as irregular motion; periodic and chaotic patterns are detected using bifurcation diagrams, the Fourier spectra, Poincaré sections, and 0‐1 test for chaos.

The paper examines the development and testing of an electro-pneumatic device for wound healing therapy after surgery in the neck area. The device generates air pressure values in a miniaturized cuff using electronic circuitry to drive an electro-valve and air compressor. The device works in two distinct modes: continuous pressure mode and pulsating pressure mode. The pressure value setting can vary from 3 to 11 mmHg, and the pulsating pressure mode’s operating frequency range is approximately 0.1 to 0.3 Hz. Laboratory measurements were conducted to evaluate the device’s correct functioning in both continuous and pulsating pressure modes. A four-day prospective study with animals ( n = 10) was also conducted to evaluate neck wound healing therapy using the electro-pneumatic device. Out of the twelve histological parameters analysed to reveal the differences between the experimental and control wounds, only one demonstrated a significant difference. Out of the ten animals treated with the device, three showed a significant difference in terms of benefit after therapy. We can therefore conclude that the device potentially improves the wound healing process in the neck area if the pre-set air pressure value does not exceed 8 mmHg.

One of the many processes in the human body on which our lives depend is the proper propagation of the electrical signal in the heart tissue. This propagation is dependent on the work of each heart cell, and even small variations in the synchronous work of these cells can lead to life-threatening conditions. A proper understanding of cardiac electrophysiology is therefore essential to understanding heart function and treating heart disease. In this work, cardiac electrophysiology is investigated using a mathematical model of a human ventricular cell (Bueno-Orovio-Cherry-Fenton model). This model is paced by regular stimulation impulses, and its responses to this stimulation are analyzed in terms of their dynamic properties, and the dependence of its dynamic parameters for the frequency and amplitude of stimulation. For this analysis, classical and modern tools from the field of dynamic systems theory (e.g. entropy measures, Fourier spectra, the 0-1 test for chaos) are used.