  Although a seizure event represents a major deviation from a baseline
electroencephalographic signal, there are features of seizure morphology that
can be seen in non-epileptic portions of the record. A transient decrease in
frequency, referred to as slowing, is a generally abnormal but not necessarily
epileptic EEG variant. Seizure termination is often associated with a period of
slowing between the period of peak amplitude and frequency of the seizure and
the return to baseline. In annotation of seizure events in the TUH EEG Seizure
Corpus, independent slowing events were identified as a major source of false
alarm error. Preliminary results demonstrated the difficulty in automatic
differentiation between seizure events and independent slowing events. The TUH
EEG Slowing database, a subset of the TUH EEG Corpus, was created, and is
introduced here, to aid in the development of a seizure detection tool that can
differentiate between slowing at the end of a seizure and an independent
non-seizure slowing event. The corpus contains 100 10-second samples each of
background, slowing, and seizure events. Preliminary experiments show that 77%
sensitivity can be achieved in seizure detection using models trained on all
three sample types compared to 43% sensitivity with only seizure and background
samples.
