Detecting Events in Video Sequence of Video-Eeg Monitoring

Abstract

<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>

Frequency Features for Detecting Events in Video Sequence of Video-Eeg Monitoring Data

“…Analysis of publications in the domain of detecting seizures from video sequences showed that the most common approach is based on the analysis of optical flow. In works (Murashov, 2019;Murashov, 2019a) it was proposed to detect diagnostic events using the measure, characterizing the degree of mobility of the region of interest. The region of interest is the part of the frame where the patient is located.…”

“…Previously, the authors of (Murashov, 2019;Murashov, 2019a) proposed to detect events in a video recording by the magnitude of the optical flow, which characterizes the degree of mobility of the frame area in which the patient is located. The algorithm was designed to detect both convulsive and non-convulsive seizures.…”

“…In (Murashov, 2020), an algorithm for the synchronous analysis of the EEG signal and video recording for the detection and differentiation of diagnostic and artifact events was proposed. The algorithm combines a threshold detector of brain activity using the ridges of wavelet spectrograms (Obukhov, 2018) and a threshold detector of events in terms of optical flow (Murashov, 2019;Murashov, 2019a). The results of the analysis of clinical data recorded on EBNeuro equipment and Galileo NT Line package software showed the fundamental possibility of reliable distinguishing artifact events from epileptic seizures (Murashov, 2020).…”

An algorithm for detecting events in video EEG monitoring data of patients with craniocerebral injuries

“…Currently, several methods have been proposed for the automatic detection of seizures from EEG data [1 -5]. In [6,7], the authors proposed an algorithm for automatic detection of seizures based on the analysis of quantitative characteristics of facial expressions in video sequences. In a video sequence using the magnitude of the optical flow, a group of frames with high scene dynamics is detected.…”

“…In this paper, we propose an algorithm for synchronous analysis of video sequences and EEG signals, based on a combination of previously developed methods described in [4,6,7], which allows differentiating an epileptic seizure from artifacts caused by chewing and moving. The proposed algorithm is capable of detecting two types of diagnostic events in video EEG data taken from patients with brain injury.…”