Regularity Changes Detection Using Maximum Approximate Entropy and Particle Swarm Optimization

JUAN FELIPE RESTREPO; GASTON SCHLOTTHAUER; MARÍA EUGENIA TORRES

San Carlos de Bariloche

Congreso; XV Reunión de Trabajo en Procesamiento de la Información y Control RPIC 2013; 2013

Universidad Nacional de Río Negro

Approximate entropy (ApEn) has proved to be a useful tool to discern between different dynamics when there is available short-length noisy data. However, in its estimations, the incorrect parameter selection (embedding dimension m and tolerance r) can undermine its discrimination capacity. Among the family of ApEn statistics, ApEn_max has been proposed as a reliable complexity estimator despite its calculation requires high computational efforts. Here we propose amethod that not only improves the discrimination capacity between different dynamics but also can reduce the time spent in calculations. This method, based on the Particle Swarm Optimization algorithm, uses ApEn_max along with the r value at which ApEn_max is achieved (r_max) as discriminating features. We test its performance through simulations with synthetic signals from non-linear models and with a real electroencephalography signal (EEG). The results show that aswell as ApEn_max, r_max can also be used as a feature to discern between dynamics. Further, using together the information provided by both estimators, the method achieves better discrimination capacity than using each one individually. The computational cost is reduced by the inclusion of the Particle Swarm Algorithm.