Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics

Abstract

Objective: Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification. Methods: Data was analyzed from 3,262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football, car crash, mixed martial arts). To test the classifier robustness, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards. Finally, with the classifier, type-specific, nearest-neighbor regression models were built for brain strain. Results: The classifier reached a median accuracy of 96% over 1,000 random partitions of training and test sets. The most important features in the classification included both low-frequency and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low-frequency and high-frequency ranges (e.g., the spectral densities of MMA impacts were higher in high-frequency range than in the low-frequency range). The type-specific regression showed a generally higher R^2-value than baseline models without classification. Conclusion: The machine-learning-based classifier enables a better understanding of the impact kinematics spectral density in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation.

33 pages, 5 figures

References in corpus (4)

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

Cited by in corpus (3)

• 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