Kinematics clustering enables head impact subtyping for better traumatic brain injury prediction

Abstract

Traumatic brain injury can be caused by various types of head impacts. However, due to different kinematic characteristics, many brain injury risk estimation models are not generalizable across the variety of impacts that humans may sustain. The current definitions of head impact subtypes are based on impact sources (e.g., football, traffic accident), which may not reflect the intrinsic kinematic similarities of impacts across the impact sources. To investigate the potential new definitions of impact subtypes based on kinematics, 3,161 head impacts from various sources including simulation, college football, mixed martial arts, and car racing were collected. We applied the K-means clustering to cluster the impacts on 16 standardized temporal features from head rotation kinematics. Then, we developed subtype-specific ridge regression models for cumulative strain damage (using the threshold of 15%), which significantly improved the estimation accuracy compared with the baseline method which mixed impacts from different sources and developed one model (R^2 from 0.7 to 0.9). To investigate the effect of kinematic features, we presented the top three critical features (maximum resultant angular acceleration, maximum angular acceleration along the z-axis, maximum linear acceleration along the y-axis) based on regression accuracy and used logistic regression to find the critical points for each feature that partitioned the subtypes. This study enables researchers to define head impact subtypes in a data-driven manner, which leads to more generalizable brain injury risk estimation.

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References in corpus (5)

• Deep Learning Head Model for Real-time Estimation of Entire Brain Deformation in Concussion
• Relationship between brain injury criteria and brain strain across different types of head impacts can be different
• Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation
• Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts
• Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football

Cited by in corpus (5)

• Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics
• Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion
• Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network
• Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation
• Identification of head impact locations, speeds, and force based on head kinematics