  Cardiovascular diseases are the leading cause of death and disability in the
world and thus their detection is extremely important as early as possible so
that it can be prognosed and managed appropriately. Hence, electrophysiological
models dealing with cardiac conduction are critically important in the field of
interdisciplinary sciences. The primary aim of this paper is to reproduce a
normal sinus rhythm ECG waveform which will act as the baseline for fitting and
then fit any clinical ECG waveform that does not deviate much from normal sinus
rhythm. To reproduce the ECG, we modeled the pacemaker complex using three
coupled van der Pol (VDP) oscillators with appropriate delays to generate the
action potentials. These action potentials are responsible for the excitation
of the non-pacemaker cells of the atria and ventricles whose electrical
activity gets recorded as the ECG signal. The ECG signal is composed of a
periodic set of individual waves corresponding to atrial and ventricular
contraction and relaxation. These waves are modeled with the help of four
FitzHugh-Nagumo (FHN) equations with impulses corresponding to the action
potentials generated by the pacemaker cells. After the successful reproduction
of a normal sinus rhythm ECG, we have developed a framework where we have used
genetic algorithm (GA) to fit a given clinical ECG data with parameters
belonging to the above mentioned system of delay differential equations (DDEs).
The GA framework has enabled us to fit ECG data representing different cardiac
conditions reasonably well. We aim to use this work to get a better
understanding of the cardiac conduction system and cardiovascular diseases
which will help humanity in the future.
