Noninvasive and Reliable Automated Detection of Spreading Depolarization in Severe Traumatic Brain Injury Using Scalp EEG

BackgroundSpreading depolarizations (SDs) are a biomarker and a potentially treatable mechanism of worsening brain injury after traumatic brain injury (TBI). Noninvasive detection of SDs could transform critical care for brain injury patients but has remained elusive. Current methods to detect SDs are based on invasive intracranial recordings with limited spatial coverage. In this study, we establish the feasibility of automated SD detection through noninvasive scalp electroencephalography (EEG) for patients with severe TBI.MethodsBuilding on our recent WAVEFRONT algorithm, we designed an automated SD detection method. This algorithm, with learnable parameters and improved velocity estimation, extracts and tracks propagating power depressions using low-density EEG. The dataset for testing our algorithm contains 700 total SDs in 12 severe TBI patients who underwent decompressive hemicraniectomy (DHC), labeled using ground-truth intracranial EEG recordings. We utilize simultaneously recorded, continuous, low-density (19 electrodes) scalp EEG signals, to quantify the detection accuracy of WAVEFRONT in terms of true positive rate (TPR), false positive rate (FPR), as well as the accuracy of estimating SD frequency.ResultsWAVEFRONT achieves the best average validation accuracy using Delta band EEG: 74% TPR with less than 1.5% FPR. Further, preliminary evidence suggests WAVEFRONT can estimate how frequently SDs may occur.ConclusionsWe establish the feasibility, and quantify the performance, of noninvasive SD detection after severe TBI using an automated algorithm. The algorithm, WAVEFRONT, can also potentially be used for diagnosis, monitoring, and tailoring treatments for worsening brain injury. Extension of these results to patients with intact skulls requires further study. Plain language summaryPhysical injury to the brain, for example due to head trauma, may worsen over time, resulting in long-term disability or death. A spreading depolarization is a slowly spreading wave in the brain, which, if detected, can be used to predict worsening brain injuries. Current methods to detect spreading depolarizations require surgeries, which are risky and unlikely to be recommended to patients with mild brain injuries. In this work, we develop an automated monitoring technique for non-surgical, non-invasive detection of spreading depolarizations, called WAVEFRONT. We validated the performance of WAVEFRONT in 12 patients with severe brain injury. Our results demonstrate the feasibility of non-invasive detection of spreading depolarizations. Our approach can potentially help clinicians predict outcomes of brain injury patients, and tailor treatments accordingly. Chamanzar et al. demonstrate the feasibility of using an automated algorithm to non-invasively detect spreading depolarizations in patients with severe traumatic brain injuries. With further testing, the algorithm could be a useful tool in the prognostication of worsening brain injuries, enabling clinicians to tailor treatments.