Asynchronous distributed microelectrode theta stimulation (ADMETS) of the hippocampus has been shown to reduce seizure frequency in the tetanus toxin rat model of mesial temporal lobe epilepsy suggesting a hypothesis that ADMETS induces a seizure resistant state. Here we present a machine learning approach to characterize the nature of neural state changes induced by distributed stimulation. We applied the stimulation to two animals under sham and ADMETS conditions and used a combination of machine learning techniques on intra-hippocampal recordings of Local Field Potentials (LFPs) to characterize the difference in the neural state between sham and ADMETS. By iteratively fitting a logistic regression with data from the inter-stimulation interval under sham and ADMETS condition we found that the classification performance improves for both animals until 90s post stimulation before leveling out at AUC of 0.64 ± 0.2 and 0.67 ± 0.02 when all inter-stimulation data is included. The models for each animal were re-fit using elastic net regularization to force many of the model coefficients to 0, identifying those that do not optimally contribute to the classifier performance. We found that there is significant variation in the non-zero coefficients between animals (p <; 0.01), suggesting that the ADMETS induced state is represented differently between subject. These findings lay the foundation for using machine learning to robustly and quantitatively characterize neural state.