In this paper, a technique for automated detecting diagnostic events in the video channel of video and electroencephalographic monitoring data is presented. The technique is based on the analysis of the quantitative features of facial expressions in images of video data. The analysis of video sequences is aimed at detecting a group of frames characterized by high activity of frame regions. For detecting the frames, a criterion computed from the optical flow is proposed. The preliminary results of the analysis of real clinical data are presented. The intervals of synchronous muscle and brain activity, which may correspond to an epileptic seizure, are detected. These intervals can be used for diagnosing epileptic seizures and distinguishing them from non-epileptic events. Requirements for video shooting conditions are formulated.

<p><strong>Abstract.</strong> In this paper, an algorithm for automated detecting diagnostic events in video channel of video and electroencephalographic (EEG) monitoring data is presented. The analysis of video sequences is focused on identifying a group of frames with high or very low (depending on the type of seizure) dynamics of informative areas according to a criterion calculated during processing of the optical flow. The preliminary results of the analysis of real clinical data are given and compared with data obtained from the synchronous EEG. The results showed the possibility in principle of reliable diagnosing epileptic seizures and distinguishing them from non-epileptic events.</p>

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One of the problems solved by analyzing the data of long-term Video EEG monitoring is the differentiation of epileptic and artifact events. For this, not only multichannel EEG signals are used, but also video data analysis, since traditional methods based on the analysis of EEG wavelet spectrograms cannot reliably distinguish an epileptic seizure from a chewing artifact. In this paper, we propose an algorithm for detecting artifact events based on a joint analysis of the level of the optical flow and the ridges of wavelet spectrograms. The preliminary results of the analysis of real clinical data are given. The results show the possibility in principle of reliable distinguishing non-epileptic events from epileptic seizures.