Classification System for Predicting Emergent Epilepsy Phenotype in the Intra-amygdala Kainic Acid Mouse Model of Epilepsy.

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 (epilepsy). The frequency of spontaneous seizures varies between mice, with some developing low or high seizure rates. The ability to predict soon after status epilepticus, which mice will go on to develop a normal frequency of seizures, would enable a significant reduction in resources and EEG reviewing time and lead to humane early end-points for the mice with low or high seizure rates.

Method: In this study, we developed two machine learning models, 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 subsequently 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).

Conclusion: 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. To date, no other research has attempted to predict emergent spontaneous seizure rates. 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.

Significance: 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. This will enable the early humane endpoint of outlier mice, which aligns with the principles of the responsible use of animals in research and simultaneously speeds up preclinical anti-epilepsy drug discovery.