Estudio de costes computacionales de métodos de modelado dinámico de señales EEG

Autores UPV
Año
CONGRESO Estudio de costes computacionales de métodos de modelado dinámico de señales EEG

Abstract

This paper presents a computational cost analysis for dynamic modeling methods considering its application to real-time biomedical applications. The analyzed methods are Dynamic Bayesian Networks (DBN) and Sequential Independent Component Analysis Mixture Modeling (SICAMM). The results show that the ICA-based methods have a lower computational cost than the BN-based methods. The applicability of these methods to patient monitoring using EEG signals is discussed besides the improvement of the time response by means of parallelization techniques.