Date of Award
Master of Science (MS)
Dr. Robert J. Schalkoff, Committee Chair
Dr. Adam Hoover
Dr. Carl W. Baum
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.
Rajendran, Sharan, "Identification and Use of PSD-Derived Features for the Contextual Detection and Classification of EEG Epileptiform Transients" (2016). All Theses. 2442.