Date of Award
Master of Science (MS)
Electrical and Computer Engineering (Holcomb Dept. of)
Dr. Robert J Schalkoff, Committee Chair
Dr. Carl Baum
Dr. Brian C. Dean
EEG is the most common test done by neurologists to study a patient’s brainwaves for pre-epileptic conditions. This thesis explains an end-to-end deep learning approach for detect-ing segments of EEG which display abnormal brain activity (Yellow-Boxes) and further classifying them to AEP (Abnormal Epileptiform Paroxysmals) and Non-AEP. It is treated as a binary and a multi-class problem. 1-D Convolution Neural Networks are used to carry out the identiﬁcation and classiﬁcation. Detection of Yellow-Boxes and subsequent analysis is a tedious process which can be fre-quently misinterpreted by neurologists without neurophysiology fellowship training. Hence, an au-tomated machine learning system to detect and classify will greatly enhance the quality of diagnosis. Two convolution neural network architectures are trained for the detection of Yellow-Boxes as well as their classiﬁcation. The ﬁrst step is detecting the Yellow-Boxes. This is done by training convolution neural networks on a training set containing both Yellow-Boxed and Non-Yellow Boxed segments treated as a 2 class problem, and is also treated as a class extension to the classiﬁcation of the Yellow-Boxes problem. The second step is the classiﬁcation of the Yellow-Boxes, where 2 diﬀerent architectures are trained to classify the Yellow-Boxed data to 2 and 4 classes. The over-all system is validated with the entire 30s EEG segments of multiple patients, which the system classiﬁes as Yellow-Boxes or Non-Yellow Boxes and subsequent classiﬁcation to AEP or Non-AEP, and is compared with the annotated data by neurologists.
Ganta, Ashish, "Detection and Classification of Epileptiform Transients in EEG Signals Using Convolution Neural Network" (2017). All Theses. 2759.