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

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@article{IJASEIT8296,
   author = {Francesca Silvestri and Simone Acciarito and Gauray Mani Khanal},
   title = {Relationship between Mathematical Parameters of Modified Van der Pol Oscillator Model and ECG Morphological Features},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {2},
   year = {2019},
   pages = {601--608},
   keywords = {heart model; Van der Pol; FitzHugh-Nagumo; relaxation oscillator; fitting neural network; classification.},
   abstract = {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 modeling 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 non-linear trends characterize the correlation functions between ECG morphological features and model parameters and that the fitting neural networks can generalized this trend by providing the model parameters given in input the respectively ECG feature.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8296},
   doi = {10.18517/ijaseit.9.2.8296}
}

EndNote

%A Silvestri, Francesca
%A Acciarito, Simone
%A Khanal, Gauray Mani
%D 2019
%T Relationship between Mathematical Parameters of Modified Van der Pol Oscillator Model and ECG Morphological Features
%B 2019
%9 heart model; Van der Pol; FitzHugh-Nagumo; relaxation oscillator; fitting neural network; classification.
%! Relationship between Mathematical Parameters of Modified Van der Pol Oscillator Model and ECG Morphological Features
%K heart model; Van der Pol; FitzHugh-Nagumo; relaxation oscillator; fitting neural network; classification.
%X 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 modeling 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 non-linear trends characterize the correlation functions between ECG morphological features and model parameters and that the fitting neural networks can generalized this trend by providing the model parameters given in input the respectively ECG feature.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8296
%R doi:10.18517/ijaseit.9.2.8296
%J International Journal on Advanced Science, Engineering and Information Technology
%V 9
%N 2
%@ 2088-5334

IEEE

Francesca Silvestri,Simone Acciarito and Gauray Mani Khanal,"Relationship between Mathematical Parameters of Modified Van der Pol Oscillator Model and ECG Morphological Features," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 2, pp. 601-608, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.2.8296.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Silvestri, Francesca
AU  - Acciarito, Simone
AU  - Khanal, Gauray Mani
PY  - 2019
TI  - Relationship between Mathematical Parameters of Modified Van der Pol Oscillator Model and ECG Morphological Features
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 2
Y2  - 2019
SP  - 601
EP  - 608
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - heart model; Van der Pol; FitzHugh-Nagumo; relaxation oscillator; fitting neural network; classification.
N2  - 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 modeling 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 non-linear trends characterize the correlation functions between ECG morphological features and model parameters and that the fitting neural networks can generalized this trend by providing the model parameters given in input the respectively ECG feature.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8296
DO  - 10.18517/ijaseit.9.2.8296

RefWorks

RT Journal Article
ID 8296
A1 Silvestri, Francesca
A1 Acciarito, Simone
A1 Khanal, Gauray Mani
T1 Relationship between Mathematical Parameters of Modified Van der Pol Oscillator Model and ECG Morphological Features
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 2
YR 2019
SP 601
OP 608
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 heart model; Van der Pol; FitzHugh-Nagumo; relaxation oscillator; fitting neural network; classification.
AB 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 modeling 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 non-linear trends characterize the correlation functions between ECG morphological features and model parameters and that the fitting neural networks can generalized this trend by providing the model parameters given in input the respectively ECG feature.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8296
DO  - 10.18517/ijaseit.9.2.8296