Authors:

Sahil Vijay Desai

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About Paper:

Auditory evoked potentials (AEPs) are a current standard of care for diagnosing hearing loss (HL). For these assessments, patients are presented with diagnostic auditory stimuli, and their neural responses are recorded to identify neurologic or otologic abnormalities. One challenge of this approach is misinterpreting similarities within types of HL. A patient with noise-induced hearing loss (NIHL) could present with a loss in hearing sensitivity; however, the cause of this damage could be from various stimuli, such as small firearms-like noise (SAF) or high-pressure explosion noise (blast). With each stimulus requiring distinct therapeutic profiles, clinicians can struggle with current diagnostics to determine proper treatment strategies and restore hearing functionality. This study attempts to distinguish types of NIHL by identifying biomarkers to classify each exposure. Rat subjects were divided into three groups and exposed to either blast, SAF, or sham noise. Groups were monitored for 56 days after the initial exposure, and traditional and non-traditional diagnostic stimuli were used to measure auditory responses during this period. Results have shown that AEPs in SAF exposures have significantly decreased transmissions from the auditory nerve and inferior colliculus compared to sham exposures. Further data is being analyzed for the blast group, and our combined results are being used to train a machine learning algorithm to correlate given AEPs to their respective NIHL type and the number of days since their exposure. Future work includes enhancing the robustness of our model by correlating anatomical changes in cochlear and brain tissue with our physiological data. Keywords: Noise-Induced Hearing Loss; Small Firearms-Like Noise; Auditory Evoked Potentials; Blast Noise Exposure; Machine Learning

Source:

Purdue University / 2024

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Co-authors:

Sahil Vijay Desai