Date

1-1-2020

Document Type

Thesis

Degree

Master of Science

Department

Electrical Engineering

First Adviser

Yevgeny . Berdichevsky

Abstract

The dynamic nature of neural activity makes prediction of epileptic seizures from electrophysiological recordings a difficult task. Understanding the states of the neural networks may lead to improvements in the prediction, control, and modeling of electrographic seizures. We explored the stability and evolution of epileptic seizures from hippocampal slices cultured on multielectrode arrays (MEA). The device recorded stable seizures that repeated for several hours. Three of the hippocampal slices that displayed continuous seizure activity for 24 hours were analyzed for state changes. Small changes in spike timings between seizures (or jitter) were calculated to quantify the transition between different states. The jitter was very small (<20%) when the seizure remained in the same state, but the jitter between seizures increased as the seizure changed in shape or frequency.

Available for download on Friday, January 29, 2021

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