Different Approaches for Linear and Non-linear ECG Generation

Developing a mathematical model for the artificial generation of electrocardiogram (ECG) signals is a subject that has been widely investigated. One of the challenges is to generate ECG signals with a wide range of waveforms, power spectra and variations in heart rate variability (HRV)-all of which are important indexes of human heart functions. In this paper we present a comprehensive model for generating such artificial ECG signals. In the first model, the operator can specify the mean and standard deviation of the heart rate, the morphology of the PQRST cycle, and the power spectrum of the RR tachogram. In the second one, we use a new modified Zeeman model for generating the time series for HRV, and a single cycle of ECG is produced by using a simple neural network. The importance of the work is the model's ability to produce artificial ECG signals that resemble experimental recordings under various physiological conditions.In one of these models, IPFM box was used to generate the R-R intervals. On the other hand, we use A dynamical model based on three coupled ordinary differential equations is introduced which is capable of generating realistic synthetic electrocardiogram (ECG) signals. In this paper we focus on clustering data derived from autoregressive moving average (ARMA) models using fc-means and fc-medoids algorithms with the Euclidean distance between estimated model parameter. These models employed to assess biomedical signal processing techniques which are used to compute clinical statistics from the ECG.