International Journal on Advanced Science, Engineering and Information Technology, Vol. 9 (2019) No. 2, List of accepted papers, DOI:10.18517/ijaseit.9.2.8296

Relationship between Mathematical Parameters of Modified Van der Pol Oscillator Model and ECG Morphological Features

Francesca Silvestri, Simone Acciarito, Gauray Mani Khanal


The mathematical model describes the electrical and mechanical activity of the cardiac conduction system thought set of differential equations. By changing the value of parameters included in these equations, it is possible to change the amplitude and the period of ECG waves. Although this model is a powerful tool for modelling the electrical activity of the heart, its use is often limited to those familiar with the differential equations that describe the system. The purpose of this work is to provide a system that allows generating an ECG signal using Ryzhii model without knowing the details of differential equations. First, we provide the relationships between the ECG wave features and the model parameters, then we generalize them through fitting neural networks. Finally, putting in series fitting neural network and heart model, we provide a system that allows generating a synthetic signal by setting as input only the morphological ECG feature. We computed numerical simulation in Simulink environment and implemented the fitting neural networks in Matlab. Results show that the correlation functions between ECG morphological features and model parameters are characterized by non-linear trends and that the fitting neural networks are able to generalised this trend by providing the model parameters given in input the respectively ECG feature.


Heart model; Van der Pol; FitzHugh-Nagumo; Relaxation oscillator; Fitting Neural Network; Classification.

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