A comprehensive model using modified Zeeman model for generating ECG signals

Developing a mathematical model for the artificial generation ofelectrocardiogram (ecg) signals is a subject that has been widely investigated. one of itsuses is for the assessment of diagnostic ecg signal processing devices. so the modelshould have the capability of producing a wide range of ecg signals, with all the nuancesthat reflect the sickness to which humans are prone, and this would necessarily includevariations in heart rate variability (hrv). in this paper we present a comprehensive modelfor generating such artificial ecg signals. we incorporate into our model the effects ofrespiratory sinus arrhythmia, mayer waves and the important very low frequencycomponent in the power spectrum of hrv. we use the new modified zeeman model forgenerating the time series for hrv, and a single cycle of ecg is produced using a radialbasis function neural network.