Parametric Modeling of Electrocardiograms using Particle Swarm Optimization

The electrocardiogram (ECG) is commonly used to monitor or diagnose adverse heart conditions. While general ECG recordings are widely available, parametric ECG models have been proposed to generate ECG-like signals. Such ECG generators can create extended segments of specific beat morphology or cardiac rhythm, especially in disease states, which can be used to validate cardiac devices or evaluate ECG processing algorithms. Furthermore, if the parameters can be fit to a variety of ECGs, these models are valuable tools in ECG compression and modeling. In this paper we propose a framework to fit parameter values of an ECG generator such that the generated signal is similar to a reference signal. We first design a parametric ECG generator with relatively minimal assumptions of single beat waveform morphology. We then use Particle Swarm Optimization to find ideal values for parameters of our ECG generator which minimize the percent root mean square difference (PRD) between the reference and generated signals. We were able to capture waveform morphologies of normal, idioventricular, and ventricular flutter rhythms with Pearson correlation coefficients above 0.9 between generated and pre-recorded signals from the MIT-BIH database.