Epileptiform Activity Detection and Classification Algorithms of Rats with Post-traumatic Epilepsy

Abstract

In this paper, the problem of epileptiform activity in EEG of rats before and after Traumatic Brain Injury is considered. Experts in neurology performed a manual markup of signals as Epileptiform Discharges and Sleep Spindles. A proprietary Event Detection Algorithm based on time-frequency analysis of wavelet spectrograms was created. Feature space from PSD and Frequency of a detected event was created, 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 on Monte-Carlo simulation of cross-validations. It was showed 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 records of rats and analysis of disease dynamics.