EEG

Seizures and seizure-like rhythmic and periodic brain activity known as “ictal-interictal-injury continuum” (IIIC) patterns are frequently detected during brain monitoring with electroencephalography (EEG) in patients with epilepsy or critical illness. Prior efforts to automate detection of IIIC patterns have been limited by lack of large well-annotated datasets to train/evaluate algorithms, and there have been only a few attempts to detect IIIC events other than seizures. The IIIC dataset includes 50,697 labeled EEG samples from 2,711 patients’ and 6,095 EEGs that were annotated by physician experts from 18 institutions. These samples were used to train SPaRCNet (Seizures, Periodic and Rhythmic Continuum patterns Deep Neural Network), a computer program that classifies IIIC events with an accuracy matching clinical experts. Prior efforts to automate seizure detection have been limited by lack of large well-annotated datasets to train and evaluate algorithms, and there have been only a few attempts to detect IIIC events other than seizures. To address this gap, we created a set of 50,697 IIIC and non-IIIC events from 2,711 patients’ (6,095 EEGs) and obtained independent annotations from 124 raters