The Johns Hopkins University

Whiting School of Engineering

Department of Electrical and Computer Engineering

PREDICTING MECHANISMS OF ARRHYTHMIA IN CARDIAC VENTRICULAR TISSUE USING HIGH PERFORMANCE COMPUTING

A Dissertation Defense by

Tabish Almas

Graduate Research Assistant

Electrical and Computer Engineering

Abstract:

Differences in electrophysiological properties of cardiac cells produce repolarization gradients across the transmural wall. Under pathological conditions, such as Long QT Syndrome (LQTS) and Heart Failure (HF), these gradients are amplified and can form the substrate for re-entry, arrhythmia and possible sudden cardiac death which remains a leading cause of death in the western world. It is therefore important to work towards a more complete understanding of the mechanisms of arrhythmia so that more effective diagnostic and therapeutic procedures can be developed. The work presented in this study describes computational approaches for developing cardiac models which can be used in conjunction with experiments to test hypotheses by which arrhythmias may arise in the heart. Particular emphasis is placed on the role of repolarization abnormalities in the generation of arrhythmias under conditions of HF.

There are two main components of our model: a) a description of the detailed anatomical structure of the tissue; and b) experimentally-derived electrophysiological models of ion currents. The computational domain consists of millions of mesh points each representing a cardiac myocyte. Current fluxes within each myocyte were modeled using 49 coupled non-linear ordinary differential equations (ODE). To overcome the need of using extremely small integration time steps due to the “stiffness” of the ODE system, we adopted a stiff symmetric operator-splitting scheme that is used widely in atmospheric and combustion simulations. In this scheme, the reaction term was solved using a stiff integration method, and the diffusion term was solved by second order Runge-Kutta method. This scheme increased computational speed by almost ten folds compared to a simple numerical method such as the forward Euler method. High Performance Computing (HPC) was employed to cut down the simulation runtimes. The parallel algorithm was highly scalable to the problem size as well as the number of processors, and could be ported to other parallel machines.

Models were used to examine HF-related functional changes in electrical conduction and action potential duration (APD) gradients. Models predict that the interplay between HF-induced electrophysiological remodeling of cell properties and reduced conductivity significantly increases repolarization gradient and APD dispersion (APDD) which have been shown to increase the risk of arrhythmias. Additionally, models are able reproduce the experimentally observed characteristics under normal and diseased conditions. These models therefore can serve as computational tools to study the mechanisms underlying cardiac arrhythmias.

Thursday March 27, 2008

1:00 p.m.

NEB 150

FOR DISABILITY INFORMATION

CONTACT: Candace Abel (410) 516-7031 cabel@jhu.edu