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.
Neural Networks for the Parameters Characterization of ECG Dynamical Model / M., Cacciola; Morabito, Francesco Carlo; Versaci, Mario; LA FORESTA, Fabio. - 193 (1):(2009), pp. 40-49. (Intervento presentato al convegno WIRN 2008 tenutosi a Vietri S. M. (SA), Italy nel May 22-24) [10.3233/978-1-58603-984-4-40].
Neural Networks for the Parameters Characterization of ECG Dynamical Model
MORABITO, Francesco Carlo;VERSACI, Mario;LA FORESTA, Fabio
2009-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
