Dataset Information

Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator.

ABSTRACT: Head motion induced by impacts has been deemed as one of the most important measures in brain injury prediction, given that the vast majority of brain injury metrics use head kinematics as input. Recently, researchers have focused on using fast approaches, such as machine learning, to approximate brain deformation in real time for early brain injury diagnosis. However, training such models requires large number of kinematic measurements, and therefore data augmentation is required given the limited on-field measured data available. In this study we present a principal component analysis-based method that emulates an empirical low-rank substitution for head impact kinematics, while requiring low computational cost. In characterizing our existing data set of 537 head impacts, each consisting of 6 degrees of freedom measurements, we found that only a few modes, e.g., 15 in the case of angular velocity, is sufficient for accurate reconstruction of the entire data set. Furthermore, these modes are predominantly low frequency since over 70% of the angular velocity response can be captured by modes that have frequencies under 40 Hz. We compared our proposed method against existing impact parametrization methods and showed significantly better performance in injury prediction using a range of kinematic-based metrics-such as head injury criterion (HIC), rotational injury criterion (RIC), and brain injury metric (BrIC)-and brain tissue deformation-based metrics-such as brain angle metric (BAM), maximum principal strain (MPS), and axonal fiber strains (FS). In all cases, our approach reproduced injury metrics similar to the ground truth measurements with no significant difference, whereas the existing methods obtained significantly different (p < 0.01) values as well as substantial differences in injury classification sensitivity and specificity. This emulator will enable us to provide the necessary data augmentation to build a head impact kinematic data set of any size. The emulator and corresponding examples are available on our website.

SUBMITTER: Arrue P

PROVIDER: S-EPMC7546353 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator.

Arrué Patricio P Toosizadeh Nima N Babaee Hessam H Laksari Kaveh K

Frontiers in bioengineering and biotechnology 20200925

Head motion induced by impacts has been deemed as one of the most important measures in brain injury prediction, given that the vast majority of brain injury metrics use head kinematics as input. Recently, researchers have focused on using fast approaches, such as machine learning, to approximate brain deformation in real time for early brain injury diagnosis. However, training such models requires large number of kinematic measurements, and therefore data augmentation is required given the limi ...[more]

PMID: 33102454

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Project description:Highlights • Trained on 80% data and tested on the remaining 20% data of 3262 impacts from 6 different impact types, a random forest kinematics classifier reached a median accuracy of 96% over 1000 random dataset partitions.• By interpreting the important classification features, the spectral characteristics of various head impact types are investigated.• With the classifier, type-specific nearest-neighbor regression models were developed for 3 injury risk metrics: 95th percentile maximum principal strain, 95th percentile maximum principal strain in corpus callosum, and cumulative strain damage. These type-specific models with classification showed higher R2 values than the baseline model without subtyping. Background 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 were analyzed from 3262 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 1000 random partitions of training and test sets. The most important features in the classification included both low- and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low- and high-frequency ranges (e.g., the spectral densities of mixed martial arts impacts were higher in the high-frequency range than in the low-frequency range). The type-specific regression showed a generally higher R2 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. Graphical Abstract Image, graphical abstract

Dataset Information

Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator.

Publications

Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator.

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