China
Africa
Explore chapters and articles related to this topic
The Use of Spectral Methods in Bidomain Studies
Published in Theo C. Pilkington, Bruce Loftis, Joe F. Thompson, Savio L-Y. Woo, Thomas C. Palmer, Thomas F. Budinger, High-Performance Computing in Biomedical Research, 2020
Natalia Trayanova, Theo Pilkington
In this study, the electrical behavior of cardiac tissue is represented by the bidomain model. The tissue is considered as passive, i.e., steady-state solutions corresponding to long current pulses are sought since the passive membrane response must be understood before more complex questions can be addressed. In accordance with the bidomain concept, the intra- and extracellular spaces are characterized by uniform anisotropic conductivities assigned along (longitudinal) and across (transverse) the fibers. The intracellular conductivity incorporates the average contribution of cytoplasm and junction in each direction.
Multisite pacing and myocardial scars: a computational study
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2020
Mohammad Albatat, Jacob Bergsland, Hermenegild Arevalo, Hans Henrik Odland, Samuel Wall, Joakim Sundnes, Ilangko Balasingham
The propagation of the electrical signal through the tissue was computed using the bidomain model (Tung 1978) where is the total ionic current given by the cell electrophysiology model, is the extracellular potential and and are the intracellular and extracellular conductivity tensors, respectively. The conductivity values are given in (Sundnes et al. 2007) and scaled with cell membrane capacitance and surface to volume ratio. This ratio was adjusted to produce the normal conduction velocity of a human LV of about 0.5 m/sec (Klabunde 2011). To capture the rapid dynamics of the electrical activity, (2a)–(2b) were solved on a mesh that was refined by a factor of two in all directions, resulting in 44,888 elements.
Computational imaging of the cardiac activities using magnetocardiography
Published in Journal of Medical Engineering & Technology, 2019
Simulated source activities were designed by checking their impact on the MCG data. In source model, there are two main possibilities of extracting the activities: epicardial potentials and transmembrane potentials. Epicardial distribution model is widely applied source model for the inverse problem of ECG, but the properties of the discrete sampling and the conductivity layers in the volume cause the problem of estimating the epicardial sources from the ECG potentials to be ill-posed. This ill-posed nature is subtle even to small noise of the measured data. The other source model, uniform double layer models define the heart activities in terms of transmembrane potentials distributed over the surface. The sources are derived from the bidomain model of the heart: intra and extracellular domain that models currents in a pattern of dipoles. This type of source model considers the anisotropic conductivity of the surrounding layers to solve the inverse problem and are less prone to noise unlike epicardial potential models thus making it less ill-posed. In transfer matrix, current distributions were assigned to Discretised heart and the torso meshes using FEM. In this study, the nodes (heart cells) were activated one at a time using Identity matrix. This is to generalise a matrix that relates the spatial relation between the sources and the MCG detectors. The location of the detector plane can be varied spatially. As the sensor plane moves near the subject’s torso, the impression of the field recorded will be strong, and it becomes weak when the distance is far. This effects in construction of the transfer matrix. The distance between the subject and the sensor plane was adjusted to ∼10 in the simulation. This measurement is considered as a moderate one in the MCG labs which constructs good transfer matrix. However, there is no standard distance measure and it varies for different subjects. We derived the magnetometer type detectors in the study and recorded the MCG data. The main advantage of MCG based inverse problem was that it has good localisation accuracy and considers both primary and secondary currents in solving the inverse problem.