Determination of Epileptic Seizure Onset from EEG Data Using Spectral Analysis and Discrete Finite Automata

This paper is a continuation of the goal to connect power spectra and Deterministic Finite Automata (DFA) in a manner to enhance the detection of spikes and seizures in epileptiform activity from Electroencephalograms (EEG). The goal is to develop robust classification rules for identifying epileptiform activity in the human brain. This paper presents advancement using the author´s proprietary developed spectral analysis to link power spectra of rat EEGs experiencing epilepsy seizures with the authors DFA algorithm and their MATLAB spectral analysis. We present a system that 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. It is a common goal for those skilled in the art of epilepsy prediction to create classifiers to make rules and discretize events leading to an epileptic seizure. Herein we present a means to link time and frequency domain components from MATLAB and proprietary software to clinical epileptiform activity.