Today, Electro-Cardiogram (ECG) is considered the most important diagnostic tool in cardiology, because its extremely accuracy to reveal potential pathologic heart activities. In the context of a multi-agent system, where agents provide to monitor the health of patients in a personalized manner on the bases of different embedded modules, we propose a module developed with the aim to prevent possible hearth diseases. It is based on a Radial Basis Neural Network (RBNN) able to analyze the ECG signals and to evaluate the impact of some specific parameters for preventing heart diseases. Index Terms—ECG, Soft Computing, Multi-agent System, Radial Basis Neural Network, Cardiac Diseases.

A Radial Basis Neural Network based agent module exploiting ECG signals to prevent heart diseases

CALCAGNO S;LA FORESTA F.
2017

Abstract

Today, Electro-Cardiogram (ECG) is considered the most important diagnostic tool in cardiology, because its extremely accuracy to reveal potential pathologic heart activities. In the context of a multi-agent system, where agents provide to monitor the health of patients in a personalized manner on the bases of different embedded modules, we propose a module developed with the aim to prevent possible hearth diseases. It is based on a Radial Basis Neural Network (RBNN) able to analyze the ECG signals and to evaluate the impact of some specific parameters for preventing heart diseases. Index Terms—ECG, Soft Computing, Multi-agent System, Radial Basis Neural Network, Cardiac Diseases.
Soft Computing; Neural Networks; Multi-agent System
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/17641
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