Date of Award

8-2016

Document Type

Thesis

Degree Name

Master of Science (MS)

Legacy Department

Electrical Engineering

Committee Member

Dr. Robert J. Schalkoff, Committee Chair

Committee Member

Dr. Adam Hoover

Committee Member

Dr. Carl W. Baum

Abstract

Epilepsy is a chronic disorder, which is characterized by seizures. For diagnosis, trained neu-rologists go over the patient’s EEG (Electroencephalograph) records looking for epileptic transients. This is a tedious and long process. The objective of this thesis is to automate the procedure by developing a detector that would pick out epileptic transients containing the ”Abnormal Epileptiform Paroxysmal” (AEP) type. The process was split into detection of potential AEPs and the classification of the detected segments. The detection of potential AEPs (called Yellow Boxing) passed boxed segments of the EEG signal to be classified as to segments that contain paroxysmal activity or not. For yellow boxing potential AEPs, a neural network was trained to determine if the signal contained in a sliding window was to be yellow boxed or not. If yellow boxed, the yellow box was then classified using a neural network trained to handle the classification problem. The networks were trained based on yellow boxes (potential AEPs) marked by trained neurologists. The resulting performance of the networks was studied using sensitivity, specificity and precision as parameters. The overall performance of the detector was verified with respect to expert marked AEPs. An additional parameter, based on the detected AEP length, was also introduced for detection to overcome the drawbacks found in using specificity.

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