Abstract

Deep Neural networks (DNN) are fundamentally information processing machines, which synthesize the complex patterns in input to arrive at solutions, with applications in various fields. One major question when working with the DNN is, which features in the input lead to a specific decision by DNN. One of the common methods of addressing this question involve generation of heatmaps. Another pertinent question is how effectively DNN has captured the entire information presented in the input, which can potentially be addressed with complexity measures of the inputs. In the case of patients with intractable epilepsy, appropriate clinical decision making depends on the interpretation of the brain signals, as recorded in the form of Electroencephalogram (EEG), which in most of the cases will be recorded through intracranial monitoring (iEEG)). In current clinical settings, the iEEG is visually inspected by the clinicians to arrive at decisions regarding the location of the epileptogenic zones which is used in the determination of surgical planning. Visual inspection and decision making is a very tedious and potentially error prone approach, given the massive amount of data that need to be evaluated in a limited amount of time. We developed a DNN model to evaluate iEEG to classify signals arising from epileptic and non-epileptic zones. One of the challenges of incorporating the deep neural network tools in the medical decision making is the black box nature of these tools. To further analyze the underlying reasons for DNN's decision regarding iEEG, we used heatmapping and signal processing tools to better understand the decision-making process of DNN. We were able to demonstrate that the energy rich regions, as captured by analytical signals, is identified by DNN as potentially epileptogenic, when arriving at decisions. We explored the DNN's ability to capture the details of the signal with information theoretical approaches. We introduced a measure of confidence of DNN predictions, named certainty index, which is calculated based on the overall outputs in the penultimate layer of the network. We employed the method of Sample Entropy (SampEn) and were able to demonstrate that the DNN's prediction certainty is related to how effectively the heatmap is correlated to the SampEn of the entire signal. We explored the parameter space of the SampEn calculation and demonstrate that the relationship between SampEn and certainty of DNN predictions hold even on changing the estimation parameters. Further we were able to demonstrate that the rate of change of relationship between the DNN output and activation map, as a function of the sequential DNN layers, is related to the SampEn of the signal. This observation suggests that the speed at which DNN captures the results is directly proportional to the information content in the signal.

Notes

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Graduation Date

2023

Semester

Summer

Advisor

Gurupur, Varadraj

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Computer Engineering

Identifier

CFE0009713; DP0027820

URL

https://purls.library.ucf.edu/go/DP0027820

Language

English

Release Date

August 2023

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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