Document Type
Article
Publication Date
1-1-2019
Abstract
Traumatic brain injury is highly prevalent in the United States. However, despite its frequency and significance, 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, which are commonly used to determine patterns in large datasets, were applied to discover how changes in testing conditions affect the mechanical response of the brain. Data at various strain rates were collected from published literature and sorted into datasets based on strain rate and tension vs. compression. Self-organizing maps were used to conduct a sensitivity analysis to rank the testing condition parameters by importance. Fuzzy C-means clustering was applied to determine if there were any patterns in the data. The parameter rankings and clustering for each dataset varied, indicating that the strain rate and type of deformation influence the role of these parameters in the datasets.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Patterson, F., AbuOmar, O., Jones, M., Tansey, K., & Prabhu, R. K. (2019). Data mining the effects of testing conditions and specimen properties on brain biomechanics. International Biomechanics, 6(1), 34–46. https://doi.org/10.1080/23335432.2019.1621206. Available at: https://digitalcommons.coastal.edu/computing/
Comments
Taylor and Francis Group originally published this article.