Electrical and Computer Engineering Commons^™

Non-Parametric Classification Of Time Series Using Permutation Ordinal Statistics, Aldo Duarte Vera Tudela

LSU Master's Theses

The present thesis explores some approaches to classify time series without prior statistical information using the concept of permutation entropy. Motivated by the results from a previous published and relevant work that set similarity relationships between EEG time series, a reproduction of the proposed approach was performed giving negative results. The failure to reproduce those results led to the conclusion that the approach of building statistics from permutation patterns have to be complemented with another metric in order to be used for classification purposes. The concept of Total Variation Distance (TVD) was then used to develop three algorithms to classify …

Brain Connectivity Networks For The Study Of Nonlinear Dynamics And Phase Synchrony In Epilepsy, Hoda Rajaei

FIU Electronic Theses and Dissertations

Assessing complex brain activity as a function of the type of epilepsy and in the context of the 3D source of seizure onset remains a critical and challenging endeavor. In this dissertation, we tried to extract the attributes of the epileptic brain by looking at the modular interactions from scalp electroencephalography (EEG). A classification algorithm is proposed for the connectivity-based separation of interictal epileptic EEG from normal. Connectivity patterns of interictal epileptic discharges were investigated in different types of epilepsy, and the relation between patterns and the epileptogenic zone are also explored in focal epilepsy.

A nonlinear recurrence-based method is …