Generation of ECG signals from a reaction-diffusion model spatially discretized

Area of Science:

• Computational Biology
• Nonlinear Dynamics
• Biomedical Engineering

Background:

• The heart's electrical activity, crucial for its function, is complex and can be disrupted by various rhythm disorders.
• Simulating electrocardiogram (ECG) signals aids in understanding cardiac electrophysiology and diagnosing arrhythmias.
• Existing models may not fully capture the dynamics of both normal and pathological heart rhythms.

Purpose of the Study:

• To develop a computational model capable of generating realistic electrocardiogram (ECG) signals.
• To simulate ECG patterns for healthy individuals and patients with cardiac rhythm disorders.
• To investigate the transition from normal sinus rhythm to chaotic rhythms like ventricular fibrillation.

Main Methods:

• A discretized reaction-diffusion system was employed to create a model of cardiac pacemakers.
• The model generates three nonlinear oscillators simulating the heart's main pacemaker sites.
• The model's output was analyzed to reproduce ECG signals under various physiological and pathological conditions.

Main Results:

• The model successfully reproduced ECG signals from healthy subjects.
• The model accurately simulated ECGs from patients with several known cardiac rhythm disorders.
• ECG signals during ventricular fibrillation were shown to be chaotic, consistent with the Ruelle-Takens-Newhouse route to chaos.

Conclusions:

• The proposed reaction-diffusion based model is effective for generating diverse ECG signals.
• The model provides a valuable tool for research, medical education, and clinical testing.
• An electronic device based on this model has been developed for practical applications.