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Machine learning (ML)-based analysis of electroencephalograms (EEGs) isplaying an important role in advancing neurological care. However, thedifficulties in automatically extracting useful metadata from clinical recordshinder the development of large-scale EEG-based ML models. EEG reports, whichare the primary sources of metadata for EEG studies, suffer from lack ofstandardization. Here we propose a machine learning-based system thatautomatically extracts components from the SCORE specification fromunstructured, natural-language EEG reports. Specifically, our system identifies(1) the type of seizure that was observed in the recording, per physicianimpression; (2) whether the session recording was normal or abnormal accordingto physician impression; (3) whether the patient was diagnosed with epilepsy ornot. We performed an evaluation of our system using the publicly available TUHEEG corpus and report F1 scores of 0.92, 0.82, and 0.97 for the respectivetasks.

SCORE-IT: A Machine Learning-based Tool for Automatic Standardization of EEG Reports