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Detecting epileptogenesis in power variant domains

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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 activity in the human brain. We present advancements using the author's proprietary developed spectral analysis software to link power spectra of rat EEGs experiencing epilepsy seizures with the authors DFA algorithm and their MATLAB spectral analysis. Our system links 1) power spectra of seizures, in sleep, spike and seizure states, with 2) Deterministic Finite Automata (DFA). Combining power spectra with DFA to correctly predict and identify epileptiform activity (spikes) and epileptic seizures opens the door to creating classifiers for seizures. We also present a DFA that separates the states between seizure and nonseizure using robust testing and additional algorithms to increase the rigor when the methodology analyses noisy signals. Our results show optimal identification of seizures even when significant artifact and noise is present in the polyphonic domain. Herein we present a dual methodology that increases epileptoid identification in a noisy domain that links time and frequency domain components from MATLAB and proprietary software to clinical epileptiform activity.
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Bibliogr. 37 poz., il.
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