Deep Learning Discriminates Seizures from Normal Brain Oscillations in the Electroencephalogram of a Rat Model of Post-traumatic Epilepsy

CNN/EEG modeling can identify fundamental spectro-temporal phenotypes that reliably distinguish SWDs from convulsive seizures, indicating that SWDs are normal EEG, not to be falsely classified as non-convulsive epileptic seizures. Accurate, automated detection of convulsive seizures over months revealed PTE was rare after FPI, with low seizure frequency. Early and late (epileptic) seizures were indistinguishable within and across rats, thus suggesting similar underlying mechanisms and neuronal circuits. This commonality, however, may also obscure important differences between seizure types. CNN modeling can facilitate objective comparison of seizures across laboratories, supplementing subjective expert visual classification. The rarity of FPI-induced PTE, and the low seizure frequency in FPI-induced PTE argues that FPI is poorly suited for effectively testing anti-seizure and anti-epileptogenesis therapies.