Abstract

Convolutional neural networks (CNNs) that incorporate Long Short-term Memory (LSTM) have shown a great deal of success in recognizing preictal activity in electroencephalogram (EEG) analysis. It is postulated that the convolutional portion of the neural network (NN) is using some particular feature or set of features to determine this preictal state. In an attempt to gain a better understanding of these features, Gradient-weighted Class Activation Mapping (Grad-CAM) and augmented Gradient-weighted Class Activation Mapping (augmented Grad-CAM) are applied to the convolutional portion of patient specific neural networks trained to recognize preictal activity. While no particular set of features were consistently highlighted by augmented Grad-CAM, it was possible to discern that some EEG channels strongly influenced an EEG epoch as being correctly labeled as preictal.

Publication Date

4-15-2022

Document Type

Thesis

Student Type

Graduate

Degree Name

Electrical Engineering (MS)

Department, Program, or Center

Electrical Engineering (KGCOE)

Advisor

Daniel B. Phillips

Advisor/Committee Member

Majid Rabbani

Advisor/Committee Member

Panos P. Markopoulos

Campus

RIT – Main Campus

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