Date of Award

Spring 5-2017

Author's School

School of Engineering & Applied Science

Author's Department

Computer Science & Engineering

Degree Name

Master of Science (MS)

Degree Type

Thesis

Abstract

In traditional audiometry, a clinician seeks to estimate her patient’s auditory response through the sequential delivery of various individual tests. These tests are treated as independent and correlation is assessed after each individual test has been completed, resulting in a diagnosis. Treating tests as independent impedes both accuracy and efficiency by ignoring correlations in conditions known to influence physiological response, for instance age, genetics, and exposure to noise. This thesis advances the existing framework for audiometry via Gaussian Processes by allowing for the estimation of audiogram thresholds for both ears simultaneously. The resulting model estimates both correlated and uncorrelated right- and left-ear audiograms with higher efficiency than was previously achievable. This work lays a foundation for building further estimation between discrete psychometric spaces.

Language

English (en)

Chair

Dr. Roman Garnett

Committee Members

Dr. Roman Garnett, Dr. Dennis Barbour, Dr. Sanmay Das

Comments

Permanent URL: https://doi.org/10.7936/K7ZK5F4V

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Engineering Commons

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