Electrocardiogram synthesis using Gaussian and fourier models

Electrocardiogram (ECG) is an important tool for investigation of cardiac functions. ECG synthesis or modeling can be useful for biomedical applications involving data compressions, signal analysis and testing of medical systems. In this paper, we present a morphological modeling method of single lead ECG by two different approaches, viz., Fourier and Gaussian models. Single lead ECG data was preprocessed to remove unwanted noise and segmented in three zones, P-R, Q-R-S and S-T. The individual segments were then modeled to extract model coefficients. The residual of each segment, computed as difference between original and reconstructed samples were also modeled using Fourier model. The algorithms were validated with lead V2 from normal and Anterior Myocardial Infarction (AMI) data from Physionet. With 25 AMI datasets, the average PRDN and SNR were found to be 9.33 and 20.71 respectively with Gaussian model and 5.43 and 22.16 respectively with Fourier model. The Fourier model showed better reconstruction performance, but less memory efficient compared to the Gaussian model.