Prediction of PTZ-induced seizures using wavelet-based residual entropy of cortical and subcortical field potentials

Our proposed algorithm for seizure prediction is based on the principle that seizure build-up is always preceded by constantly changing bursting levels. We use a novel measure of residual subband wavelet entropy (RSWE) to directly estimate the entropy of bursts, which is otherwise obscured by the ongoing background activity. Our results are obtained using a slow infusion anesthetized pentylenetetrazol (PTZ) rat model in which we record field potentials (FPs) from frontal cortex and two thalamic areas (anterior and posterior nuclei). In each frequency band, except for the theta-delta frequency bands, we observed a significant build-up of RSWE from the preictal period to the first ictal event (p < or = 0.05) in cortex. Significant differences were observed between cortical and thalamic RSWE (p < or = 0.05) subsequent to seizure development. A key observation is the twofold increase in mean cortical RSWE from the preictal to interictal period. Exploiting this increase, we develop a slope change detector to discern early acceleration of entropy and predict the approaching seizure. We use multiple observations through sequential detection of slope changes to enhance the sensitivity of our prediction. Using the proposed method applied to a cohort of four rats subjected to PTZ infusion, we were able to predict the first seizure episode 28 min prior to its occurrence.