IEEE Trans Neural Syst Rehabil Eng. 2019 Jul;27(7):1492-1501. doi: 10.1109/TNSRE.2019.2922553. Epub 2019 Jun 12.
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.
人们越来越努力地了解脑震荡的神经生理影响，旨在超越目前基于主观行为评估的诊断和识别方法，因为后者的敏感性较差。最近的证据表明，脑电图（EEG）测量的事件相关电位（ERP）是过去脑震荡的持久神经生理标志物。然而，由于此类证据仅限于组水平分析，因此尚不清楚它们在个体水平上检测脑震荡的程度。一个很有前途的研究途径是使用机器学习来创建定量预测模型，以在个体中检测先前的脑震荡。在本文中，我们将脑震荡个体的 ERP 研究中的近期组水平发现转化为一种机器学习框架，用于对过去的脑震荡进行单个体预测。我们发现，单个个体 ERP 的统计信息和小波特征的组合产生了 81%的分类准确性，敏感性为 82%，特异性为 80%，优于当前的实践。值得注意的是，该模型能够检测到最后一次受伤多达 30 年前的个体中的脑震荡影响。然而，未能在一部分个体中检测到过去的脑震荡表明，在组水平分析中发现的明确影响可能无法为我们提供脑震荡神经生理影响的全貌。
