Metric Classification of Traumatic Brain Injury Epileptiform Activity from Electroencephalography Data

Abstract

This paper considers the problem of epileptiform activity recognition in EEG. We conducted experiments 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). We developed a proprietary Event Detection Algorithm based on time-frequency analysis of wavelet spectrograms. Feature space was based on Power Spectrum Density (PSD) and Frequency of signals, and each feature was assessed for importance of epileptic activity prediction. We used resulted predictors 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. We 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 before and after TBI. and analysis of disease dynamics.