Theses and Dissertations
ORCID
https://orcid.org/0009-0008-4287-1847
Issuing Body
Mississippi State University
Advisor
Gudla, Charan
Committee Member
Trawick, George
Committee Member
Veeranna, Sangam A.
Date of Degree
12-13-2024
Original embargo terms
Visible MSU only 6 months
Document Type
Graduate Thesis - Campus Access Only
Major
Computer Science (Research)
Degree Name
Master of Science (M.S.)
College
James Worth Bagley College of Engineering
Department
Department of Computer Science and Engineering
Abstract
This study focuses on the use of machine learning (ML) techniques to automate the detection of Cortical Evoked Auditory Responses (CEARs), which are key in understanding how the auditory cortex processes sound stimuli. Traditionally, analyzing these auditory responses has relied on manual interpretation by audiologists, a process that can introduce variability and human error, particularly in complex cases. To address this challenge, the research utilizes advanced deep learning models, including Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks, and Bidirectional LSTM (BiLSTM) architectures, to analyze Electroencephalography (EEG) data and classify the presence or absence of auditory responses automatically. By employing these models, the study demonstrates improved accuracy in detecting auditory responses, with the BiLSTM model achieving the highest accuracy of 90%. Additionally, the use of Grad-CAM visualizations enables better interpretability of the model's predictions, allowing for insights into the biological relevance of the EEG features the models focused on. The findings highlight the potential of ML techniques to enhance the efficiency and accuracy of auditory diagnostics, which can support audiologists in clinical decision-making. The research also paves the way for future developments, such as integrating these models into real-time EEG systems and expanding their use to other time-series data or domains like speech recognition or ECG analysis. This automation represents a significant step toward advancing auditory diagnostics and improving patient outcomes.
Recommended Citation
Beerelli, Pranavi, "Analysis of cortical evoked auditory response detection in adults using machine learning" (2024). Theses and Dissertations. 6429.
https://scholarsjunction.msstate.edu/td/6429
