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 website1.
Highlights
Traumatic brain injury (TBI) is one of the most debilitating health problems in our society today, with nearly two million new cases in the US every year (Taylor et al, 2017)
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)
We investigate the efficacy of each approximation by comparing its performance in detecting brain motion/deformation and injury prediction using three types of metrics: (1) kinematicsbased injury metrics, including HIC, RIC, and brain injury criterion (BrIC), (2) brain angle metric (BAM), and (3) tissue deformation-based finite element injury metrics, including maximum principal strain (MPS) and axonal fiber strain (FS) in the whole brain (WB) and corpus callosum (CC)
Summary
Traumatic brain injury (TBI) is one of the most debilitating health problems in our society today, with nearly two million new cases in the US every year (Taylor et al, 2017) The majority of these cases are considered mild, known as concussion (Defense and Veterans Brain Injury Center, 2018). Kinematic-based metrics such as head injury criterion (HIC) (NTSA, 1972), rotational injury criterion (RIC) (Kimpara and Iwamoto, 2012), and brain injury criterion (BrIC) (Takhounts et al, 2013) have been used to detect injury These metrics are still widely used among researchers and are endorsed by safety regulating organizations such as the National Highway Traffic Safety Administration (NHTSA) (Laituri et al, 2016) and the National Operating Committee on Standards for Athletic Equipment (NOCSAE) (National Operating Committee on Standards for Athletic, 2012). In the case of lumped models, brain angle metric (BAM), developed based on a data set of concussive and sub-concussive head impacts (Laksari et al, 2019), and in the case of FE models, maximum principal strain (MPS) and axonal fiber strain (FS) along the white matter axon fibers have been proposed as effective injury diagnosis metrics (Wu et al, 2019b)
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