Mississippi State University
Prabhu, Raj Kumar
Priddy, Lauren B.
Tansey, Keith E.
Date of Degree
Graduate Thesis - Open Access
Master of Science (M.S.)
James Worth Bagley College of Engineering
Department of Agricultural and Biological Engineering
Traumatic brain injury is highly prevalent in the United States yet there is little understanding of how the brain responds during injurious loading. A confounding problem is that because testing conditions vary between assessment methods, brain biomechanics cannot be fully understood. Data mining techniques were applied to discover how changes in testing conditions affect the mechanical response of the brain. Data were gathered from literature sources and self-organizing maps were used to conduct a sensitivity analysis to rank considered parameters by importance. Fuzzy C-means clustering was applied to find any data patterns. The rankings and clustering for each data set varied, indicating that the strain rate and type of deformation influence the role of these parameters. Multivariate linear regression was applied to develop a model which can predict the mechanical response from different experimental conditions. Prediction of response depended primarily on strain rate, frequency, brain matter composition, and anatomical region.
Crawford, Folly Martha Dzan, "Data Mining the Effects of Storage Conditions, Testing Conditions, and Specimen Properties on Brain Biomechanics" (2018). Theses and Dissertations. 1252.