Automated video detection of epileptic convulsion slowing as a precursor for post-ictal generalized EEG suppression

Rationale. Automated monitoring and alerting for adverse events in patients with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently we explored the relation between clonic slowing at the end of a convulsive seizure and the occurrence and duration of a subsequent period of post-ictal generalized EEG suppression (PGES). We found that prolonged periods of PGES can be predicted by the amount of progressive increase of inter-clonic intervals (ICI) during the seizure. PGES was previously linked to SUDEP The purpose of the present study is to develop an automated, remote video sensing based algorithm for real-time detection of significant clonic slowing that can be used to alert for PGES and which may eventually help preventing sudden unexpected death in epilepsy (SUDEP). Methods. The technique is based on our earlier published optical flow video sequence processing paradigm that has been applied for automated detection of major motor seizures. Here we introduce an integral Radon-like transformation on the timefrequency wavelet spectrum in order to detect log-linear frequency changes during the seizure. We validate the automated detection and quantification of the ICI increase by comparison to the results from manually processed EEG traces as “gold standard”. We studied 48 cases of convulsive seizures for which synchronized EEG-video recording was available. Results. In most cases the spectral ridges obtained from Gabor-wavelet transformations of the optical flow group velocities were in close proximity to the ICI traces detected manually from EEG data during seizure (the gold standard). The quantification of the slowing-down effect measured by the dominant angle in the Radon transformed spectrum was significantly correlated with the exponential ICI increase factors obtained from manual detection.