From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes

July 18, 2019

Rober BoshraKiret DhindsaOmar BoursalieKyle I. RuiterRanil SonnadaraReza SamaviThomas E. DoyleJames P. ReillyJohn F. Connolly

From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes

There has been increased effort to understand the neurophysiological effects of concussion aimed to move diagnosis and identification beyond current subjective behavioral assessments that suffer from poor sensitivity. Recent evidence suggests that event-related potentials (ERPs) measured with electroencephalography (EEG) are persistent neurophysiological markers of past concussions. However, as such evidence is limited to group-level analyzes, the extent to which they enable concussion detection at the individual-level is unclear. One promising avenue of research is the use of machine learning to create quantitative predictive models that can detect prior concussions in individuals. In this paper, we translate the recent group-level findings from ERP studies of concussed individuals into a machine learning framework for performing single-subject prediction of past concussion. We found that a combination of statistics of single-subject ERPs and wavelet features yielded a classification accuracy of 81% with a sensitivity of 82% and a specificity of 80%, improving on current practice. Notably, the model was able to detect concussion effects in individuals who sustained their last injury as much as 30 years earlier. However, failure to detect past concussions in a subset of individuals suggests that the clear effects found in group-level analyses may not provide us with a full picture of the neurophysiological effects of concussion.

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