  Electroencephalogram (EEG) is an important diagnostic test that physicians
use to record brain activity and detect seizures by monitoring the signals.
There have been several attempts to detect seizures and abnormalities in EEG
signals with modern deep learning models to reduce the clinical burden.
However, they cannot be fairly compared against each other as they were tested
in distinct experimental settings. Also, some of them are not trained in
real-time seizure detection tasks, making it hard for on-device applications.
Therefore in this work, for the first time, we extensively compare multiple
state-of-the-art models and signal feature extractors in a real-time seizure
detection framework suitable for real-world application, using various
evaluation metrics including a new one we propose to evaluate more practical
aspects of seizure detection models. Our code is available at
https://github.com/AITRICS/EEG_real_time_seizure_detection.
