Objective: Animal models of drug-resistant epilepsy represent an important resource for discovering new drug targets and testing experimental medicines. Intra-amygdala microinjection of kainic acid in mice is one of the most widely regarded models of drug-resistant epilepsy. Mice develop acute status epilepticus, which diminishes after a few hours and then, within a few days, mice display spontaneous seizures. The frequency of spontaneous seizures varies between mice, with some developing low or high seizure rates.

Methods: We developed a feature-based and transfer learning-based approach, for predicting the emergent spontaneous seizure rates in the intra-amygdala kainic acid model based on the acute EEGs recorded in mice during status epilepticus lasting 40 minutes. The method was trained on data from 28 mice and tested on data from 16 mice.

Results: The feature-based and transfer learning-based models achieved accuracies of 69% and 75%, respectively on the test set in classifying emergent epilepsy as normal or outlier (i.e. low-frequency or high-frequency seizure rate).

Conclusions: A limitation of the intra-amygdala kainic acid model has been the loss of time and resources from generating mice with low or high rates of spontaneous seizures. The feature-based and transfer learning-based models will assist researchers in identifying mice with a normal frequency of seizures before the onset of spontaneous seizures. Conclusions: We have implemented this approach as a web server, which can potentially reduce the time and resources spent analysing the EEGs of mice who develop low-frequency or high-frequency seizure rates.