Journal of Radio Electronics. eISSN 1684-1719. 2025. №11

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DOI: https://doi.org/10.30898/1684-1719.2025.11.26  

 

 

 

A New Algorithm for Detecting Motion Artifacts

in Video-EEG Monitoring Data

 

D.M. Murashov ¹, Yu.V. Obukhov ², I.A. Kershner ²,

M.V. Sinkin ³, I.V. Okuneva ³

 

¹ Federal Research Center “Computer Science and Control” of RAS,

119333, Russia, Moscow, 42 Vavilov str.

² Kotelnikov IRE RAS, 125009, Russia, Moscow, Mokhovaya str., 11, b.7

³ Sklifosovsky Research Institute for Emergency Medicine,

129090, Russia, Moscow, Bolshaya Suharevskaya Square, 3.

 

The paper was received November 6, 2025.

 

Abstract. The article proposes a new algorithm for detecting artifacts in video recordings of long-term video-EEG monitoring data in the context of the problem of diagnosing delayed cerebral ischemia after subarachnoid hemorrhage. The main idea of the new algorithm is as follows. First, to ensure the invariance of the algorithm with respect to illumination variations, we calculate the motion measures from the edge map of the region of interest. Secondly, to expand the set of motion measures by including variation of information between the blurred edge maps of two consecutive frames. Third, to combine detectors that use different measures of scene motion. An experiment on clinical video-EEG monitoring data showed that the proposed algorithm provides sensitivity of 0.94, specificity of 0.94, accuracy of 0.94, and F1 score of 0.91.  The obtained characteristics correspond to the level of known motion detectors in video-EEG data.

Key words: video-electroencephalographic monitoring; optical flow; variation of information; edge map; artifacts; cerebral ischemia; motion measure.

Financing: The study was funded by the Russian Science Foundation Grant  No. 22-69-00102, https://rscf.ru/en/project/22-69-00102/.

Corresponding author: Kershner Ivan Andreevich, ivan_kershner@mail.ru

 

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For citation:

Murashov D.M., Obukhov Yu.V., Kershner I.A., Sinkin M.V., Okuneva I.V. A new algorithm for detecting motion artifacts in video-EEG monitoring data // Journal of Radio Electronics. – 2025.  – №. 11. https://doi.org/10.30898/1684-1719.2025.11.26