Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts

• Published in: Annals of biomedical engineering Vol. 50; no. 11; pp. 1596 - 1607
• Main Authors: Zhan, Xianghao, Li, Yiheng, Liu, Yuzhe, Cecchi, Nicholas J., Gevaert, Olivier, Zeineh, Michael M., Grant, Gerald A., Camarillo, David B.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.11.2022; Springer Nature B.V
• Subjects: Angular acceleration; Biochemistry; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Classical Mechanics; Cluster analysis; Clustering; Crashes; Damage assessment; Generalized linear models; Head; Impact damage; Kinematics; Linearity; Multivariate analysis; Regression analysis; Regression models; S.I.: Concussions; Statistical models; Traffic accidents; Vector quantization; means; Traumatic brain injury; Impact clusters; Clustering; Kinematics
• ISSN: 0090-6964; 1573-9686
• DOI: 10.1007/s10439-022-03020-0

In a previous study, we found that the relationship between brain strain and kinematic features cannot be described by a generalized linear model across different types of head impacts. In this study, we investigate if such a linear relationship exists when partitioning head impacts using a data-driven approach. We applied the K -means clustering method to partition 3161 impacts from various sources including simulation, college football, mixed martial arts, and car crashes. We found piecewise multivariate linearity between the cumulative strain damage (CSDM; assessed at the threshold of 0.15) and head kinematic features. Compared with the linear regression models without partition and the partition according to the types of head impacts, K -means-based data-driven partition showed significantly higher CSDM regression accuracy, which suggested the presence of piecewise multivariate linearity across types of head impacts. Additionally, we compared the piecewise linearity with the partitions based on individual features used in clustering. We found that the partition with maximum angular acceleration magnitude at 4706 rad/s 2 led to the highest piecewise linearity. This study may contribute to an improved method for the rapid prediction of CSDM in the future.