An EEG-based hybrid machine learning approach for CT scan triage in mild traumatic brain injury

Mild traumatic brain injury (mTBI) frequently prompts computed tomography (CT) imaging in emergency departments, despite a high proportion of negative findings. Objective, non-invasive tools that can support CT triage decisions under realistic clinical constraints are therefore needed. This study evaluates whether electroencephalography (EEG)-based biomarkers combined with temporal modeling can provide reliable decision support for mTBI assessment. Resting-state EEG was acquired using a clinically feasible 19-channel montage from 120 subjects classified as CT-Abnormal, CT-Normal, or healthy controls. Automated preprocessing was applied uniformly without manual artifact rejection. Quantitative EEG biomarkers were statistically validated and used to train a Random Forest classifier, while a Long Short-Term Memory (LSTM) network modeled temporal EEG dynamics. The biomarker-based model achieved a test accuracy of 81.25%. A hybrid fusion framework integrating Random Forest and LSTM outputs improved performance, achieving an accuracy of 93.33% and enhanced sensitivity for the CT-Normal category. Generalization was confirmed on an independent test set. These findings indicate that hybrid EEG representations can support CT triage decisions in mTBI as an adjunct to existing clinical assessment.