Topological charge-density method of identifying phase singularities in cardiac fibrillation

Turbulence control in memristive neural network via adaptive magnetic flux based on DLS-ADMM technique

High-voltage defibrillation for eliminating cardiac spiral waves has significant side effects, necessitating the pursuit of low-energy alternatives for a long time. Adaptive optimization techniques and machine learning methods provide promising solutions for adaptive control of cardiac wave propagation. In this paper, the control of spiral waves and turbulence, as well as 2D and 3D heterogeneity in memristive neural network by using adaptive magnetic flux (AMF) is investigated based on dynamic learning of synchronization - alternating direction method of multipliers (DLS-ADMM). The results show that AMF can achieve global electrical synchronization under multiple complex conditions. There is a trade-off between AMF accuracy and computational speed, lowering the resolution of AMF requires a higher flux of magnetic fields to achieve the network synchronization, resulting in an increase in average Hamiltonian energy, which implies greater energy consumption. The AMF method is more energy efficient than existing DC and AC methods, but it relies on adequate resolution. The ADMM constraints can enhance the synchronization robustness and energy efficiency of DLS techniques, albeit at the cost of increased the computational complexity. The adaptive elimination of spiral waves and turbulence using AMF presented in this paper may provide a novel approach for the low-energy defibrillation studies, and its practical application and performance enhancement deserve further research.

Directed Graph Mapping exceeds Phase Mapping for the detection of simulated 2D meandering rotors in fibrotic tissue with added noise

Cardiac arrhythmias such as atrial fibrillation (AF) are recognised to be associated with re-entry or rotors. A rotor is a wave of excitation in the cardiac tissue that wraps around its refractory tail, causing faster-than-normal periodic excitation. The detection of rotor centres is of crucial importance in guiding ablation strategies for the treatment of arrhythmia. The most popular technique for detecting rotor centres is Phase Mapping (PM), which detects phase singularities derived from the phase of a signal. This method has been proven to be prone to errors, especially in regimes of fibrotic tissue and temporal noise. Recently, a novel technique called Directed Graph Mapping (DGM) was developed to detect rotational activity such as rotors by creating a network of excitation. This research aims to compare the performance of advanced PM techniques versus DGM for the detection of rotors using 64 simulated 2D meandering rotors in the presence of various levels of fibrotic tissue and temporal noise. Four strategies were employed to compare the performances of PM and DGM. These included a visual analysis, a comparison of F2-scores and distance distributions, and calculating p-values using the mid-p McNemar test. Results indicate that in the case of low meandering, fibrosis and noise, PM and DGM yield excellent results and are comparable. However, in the case of high meandering, fibrosis and noise, PM is undeniably prone to errors, mainly in the form of an excess of false positives, resulting in low precision. In contrast, DGM is more robust against these factors as F2-scores remain high, yielding F2≥0.931 as opposed to the best PM F2≥0.635 across all 64 simulations.

Topological charge-density-vector method of identifying filaments of scroll waves

Scroll waves have been found in a variety of three-dimensional excitable media, including physical, chemical, and biological origins. Scroll waves in cardiac tissue are of particular significance as they underlie ventricular fibrillation that can cause sudden death. The behavior of a scroll wave is characterized by a line of phase singularity at its organizing center, known as a filament. A thorough investigation into the filament dynamics is the key to further exploration of the general theory of scroll waves in excitable media and the mechanisms of ventricular fibrillation. In this paper, we propose a method to identify filaments of scroll waves in excitable media. From the definition of the topological charge of filaments, we obtain the discrete expression of the topological charge-density vector, which is useful in calculating the topological charge vectors at each grid in the space directly. The set of starting points of these topological charge vectors represents a set of phase singularities, thereby forming a line of phase singularity, that is, a filament of a scroll wave.