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Objective: Continuous EEG (cEEG) monitoring is associated with lowermortality in critically ill patients, however it is underutilized due to thedifficulty of manually interpreting prolonged streams of cEEG data. Here wepresent a novel real-time, machine learning-based alerting and monitoringsystem for epilepsy and seizures (RAMSES) that dramatically reduces the amountof manual EEG review. Methods: We developed a custom data reduction algorithmusing a random forest, and deployed it within an online cloud-based platformwhich streams data and communicates interactively with caregivers via a webinterface to display algorithm results. We validate RAMSES on cEEG recordingsfrom 77 patients undergoing routine scalp ICU EEG monitoring. Results: Onsubjects with seizures we achieved >80% overall data reduction, while detectinga mean of 84% of seizures across all validation patients, with 19/27 patientsachieving 100% seizure detection. On seizure free-patients, the majority ofcEEG records, we reduced data requiring manual review by >83%. Conclusion: Thisstudy validates a platform for machine-learning assisted data reduction.Significance: This work represents a meaningful step towards improving utilityand decreasing cost for cEEG monitoring We also make our high-quality annotateddataset of 77 ICU cEEG recordings public for others to validate and improveupon our methods.

RAMSES: A full-stack application for detecting seizures and reducing data during continuous EEG monitoring