Automated EEG-based prediction of delayed cerebral ischemia after subarachnoid hemorrhage

•A number of EEG features have been shown to be predictive of delayed cerebral ischemia (DCI), but a continuous assessment of multidimensional EEG features is lacking.•Combining spectral and epileptiform discharge feature information, using automated calculations, allows for dynamic prediction of DCI.•This dynamic multi-feature assessment increases the feasibility of implementing interventions in response to our EEG derived DCI risk probability. Delayed cerebral ischemia (DCI) is a leading complication of aneurysmal subarachnoid hemorrhage (SAH) and electroencephalography (EEG) is increasingly used to evaluate DCI risk. Our goal is to develop an automated DCI prediction algorithm integrating multiple EEG features over time. We assess 113 moderate to severe grade SAH patients to develop a machine learning model that predicts DCI risk using multiple EEG features. Multiple EEG features discriminate between DCI and non-DCI patients when aligned either to SAH time or to DCI onset. DCI and non-DCI patients have significant differences in alpha-delta ratio (0.08 vs 0.05, p