Abstract
Clinical decision rules such as the Canadian Head CT rule have high sensitivity but lack specificity for identifying significant intracranial findings when evaluating patients with mild traumatic brain injury (mTBI). Advances in brain electrical activity (EEG) signal processing, real-time analyses, and use of an AI/machine learning for the derivation of brain activity-based biomarkers have greatly enhanced the pragmatism of EEG clinically. High accuracy and negative predictive value have been demonstrated (n-720) using an FDA cleared brain activity-based multivariate algorithm for predicting the likelihood of intracranial injuries with ≥ 1mL blood visible on a CT scan.
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