Efficient identification of epileptiform discharges in brain scan data

Abstract

This disclosure provides techniques for efficiently identifying epileptiform discharges in brain scan data. Brain scan data from an epileptic patient is segmented into multiple windows. Features of the data are identified for each window and provided to a machine learning (ML) model trained on labeled brain scan data. The ML model ranks the windows according to the likelihood each contains an epileptiform discharge. The highest-ranked window is shown to a technician trained in interpreting brain scans. The technician provides feedback regarding whether the window contains an epileptiform discharge or not. The ML model is updated by an online learning process based on feedback from the technician. The remaining windows are re-ranked, and the next highest-ranked window is shown to the technician. This process repeats and the ML model improves based on the technician feedback. This greatly reduces the amount of technician time spent reviewing brain scan data.