Implementation of Time-Frequency Analysis for Seizure Localization

Overview

Epilepsy is generally diagnosed using electroencephalograms, or EEGs, which are tests that detect electrical activity in a person’s brain and consist of measurements of a set of potential differences between pairs of scalp electrodes. Signals recorded from living neurological tissue are extremely noisy at all scales from individual ion channels through collections of one or more neurons up to scalp-recorded EEGs. The noise recorded in these EEGs causes uncertainty in the location of the seizure-onset zones. It is not uncommon for surgeries to take place where the incorrect section of the brain is completely removed from a patient, leaving them to still have seizures. The success of focal epilepsy surgery strongly depends on accurate identification of the seizure focus, and the noise found in EEG scans obstructs the identification of these zones. It is vital to find an alternative method of analyzing and modeling EEG data to improve the technique of epilepsy diagnosis before performing invasive brain surgeries. The goal of this phase of the project is to investigate whether absence seizures can be best understood by separating the stimulus from the system using cepstral analysis of publicly available absence seizure data. If overtone strength is more clearly modeled using cepstral analysis, it could be an essential discovery to absence seizure research. This method of analysis requires taking the logarithm of the spectrogram which turns multiplied signal components (which is typically how components mix) into additive components. If the cepstrum proves to provide a more accurate analysis of EEG data, this method can be helpful in developing an objective program that can detect active or upcoming absence seizures. GEORGIA

Competition history

• JSHS 2022 Category not listed

Resources

• National JSHS abstract book ↗

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