Machine Learning Applications to Epileptiform Activity Recognition in Rats after Traumatic Brain Injury

This paper considers the problem of epileptiform activity recognition in EEG. Experiments were conducted on male rat before and after Traumatic Brain Injury (TBI). Experts in neurology performed a manual markup of signals as Epileptiform Discharges (ED) and Sleep Spindles (SS). A proprietary Event Detection Algorithm based on time-frequency analysis of wavelet spectrograms was developed. Feature space was based on Power Spectrum Density (PSD) and Frequency of signals, and each feature was assessed for importance of epileptic activity prediction. Resulted predictors were used for training logistic regression model, which estimated features weights in probability of epilepsy function. Validation of proposed model was done by multiple train-test division. It was shown that the accuracy of prediction is around 80%. Proposed Epilepsy Prediction Model, as well as Event Detection Algorithm, can be applied to identification of epileptiform activity in long term EEG records of rats and analysis of disease dynamics.