Detecting epileptogenesis in power variant domains

This paper presents the merging of two sets of experiments in the continuing endeavor to mine epileptiform activity from Electroencephalograms (EEG). The goal is to develop robust classification rules for identifying epileptiform a ctivity in the human brain. We present advancements using the author’s proprietary devel oped spectral analysis software to link power spectra of rat EEGs experiencing epilepsy seiz ur s with the authors DFA algorithm and their MATLAB spectral analysis. Our system li nks 1) power spectra of seizures, in sleep, spike and seizure states with 2) Determi nistic Finite Automata (DFA). Combining power spectra with DFA to correctly predict and id entify epileptiform activity (spikes) and epileptic seizures opens the door to creating c lassifiers for seizures. We also present a DFA that separates the states between seizure and n on-seizure using robust testing and additional algorithms to increase the rigor when the methodology analysis noisy signals. Our results show optimal identification of a seizur es even when significant artifact and noise is present in the polyphonic domain. Herein we pres ent a dual methodology that increases epileptoid identification in a noisy domain that l inks time and frequency domain components from MATLAB and proprietary software to clinica l epileptiform activity.

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