Automatic Classification of Athletes With Residual Functional Deficits Following Concussion by Means of EEG Signal Using Support Vector Machine

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2008-08, Vol.16 (4), p.327-335
• Main Authors: Cao, Cheng, Tutwiler, Richard Laurence, Slobounov, Semyon
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Algorithms; Artificial Intelligence; Athletic Injuries - diagnosis; Brain Concussion - diagnosis; Brain injuries; Brain Mapping - methods; Brain modeling; Classification algorithms; Clinical medicine; Diagnosis, Computer-Assisted - methods; Electroencephalography; Electroencephalography (EEG); Electroencephalography - methods; feature selection; Female; Frequency; Humans; Lab-on-a-chip; Male; mild traumatic brain injury (MTBI); Pattern Recognition, Automated - methods; Recruitment; Reproducibility of Results; Sensitivity and Specificity; Studies; support vector machine (SVM); Support vector machine classification; Support vector machines; Systems engineering and theory
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2008.918422

There is a growing body of knowledge indicating long-lasting residual electroencephalography (EEG) abnormalities in concussed athletes that may persist up to 10-year postinjury. Most often, these abnormalities are initially overlooked using traditional concussion assessment tools. Accordingly, premature return to sport participation may lead to recurrent episodes of concussion, increasing the risk of recurrent concussions with more severe consequences. Sixty-one athletes at high risk for concussion (i.e., collegiate rugby and football players) were recruited and underwent EEG baseline assessment. Thirty of these athletes suffered from concussion and were retested at day 30 postinjury. A number of task-related EEG recordings were conducted. A novel classification algorithm, the support vector machine (SVM), was applied as a classifier to identify residual functional abnormalities in athletes suffering from concussion using a multichannel EEG data set. The total accuracy of the classifier using the 10 features was 77.1%. The classifier has a high sensitivity of 96.7% (linear SVM), 80.0% (nonlinear SVM), and a relatively lower but acceptable selectivity of 69.1% (linear SVM) and 75.0% (nonlinear SVM). The major findings of this report are as follows: 1) discriminative features were observed at theta, alpha, and beta frequency bands, 2) the minimal redundancy relevance method was identified as being superior to the univariate -test method in selecting features for the model calculation, 3) the EEG features selected for the classification model are linked to temporal and occipital areas, and 4) postural parameters influence EEG data set and can be used as discriminative features for the classification model. Overall, this report provides sufficient evidence that 10 EEG features selected for final analysis and SVM may be potentially used in clinical practice for automatic classification of athletes with residual brain functional abnormalities following a concussion episode.