Neural Networks for the Parameters Characterization of ECG Dynamical Model

Authors:
Matteo Cacciola;Fabio La Foresta;Francesco Carlo Morabito;Mario Versaci
Affiliations:
University “Mediterranea” of Reggio Calabria, DIMET Via Graziella Feo di Vito, 89100 Reggio Calabria, Italy;University “Mediterranea” of Reggio Calabria, DIMET Via Graziella Feo di Vito, 89100 Reggio Calabria, Italy;University “Mediterranea” of Reggio Calabria, DIMET Via Graziella Feo di Vito, 89100 Reggio Calabria, Italy;University “Mediterranea” of Reggio Calabria, DIMET Via Graziella Feo di Vito, 89100 Reggio Calabria, Italy
Venue:
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Year:
2009

Citing 3
Cited 0

Numerical methods for ordinary differential systems: the initial value problem

Numerical methods for ordinary differential systems: the initial value problem
Advanced Methods And Tools for ECG Data Analysis

Advanced Methods And Tools for ECG Data Analysis
Multichannel ECG and noise modeling: application to maternal and fetal ECG signals

EURASIP Journal on Applied Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Electrocardiogram (ECG) is the recording of the effects produced from the bioelectric field generated by the cardiac muscle during its activity. Specific changes in ECG signals can reveal pathologic heart activity. For this reason, a dynamic model-that accurately describes the heart bioelectric behavior and that can be mathematically analyzed-could be a practical way to investigate heart diseases. The aim of this paper is to introduce a dynamic model to simulate pathological ECG as well as to evaluate an Artificial Neural Network able to distinguish the impact of some modeling parameters on specific and peculiar features of EGC's trend.