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 sig nals with modern deep learning models to re duce 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. 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 evalu ation metrics including a new one we propose
to evaluate more practical aspects of seizure de tection models.