Work

A New Algorithm for Automated Diagnosis of Delayed Cerebral Ischemia in Video-EEG Monitoring Data

Year: 2025

Type: article

Abstract: . The paper proposes a new algorithm for automated diagnosis of delayed cerebral ischemia in video-EEG monitoring data. The algorithm uses the indicator of the delayed cerebral ischemia which is the number of epileptiform discharges per hour over a sufficiently long observation interval. It combines a detector of epileptiform discharges and a new motion artifact detector. The epileptiform discharge detection algorithm analyzes the cross-correlation function of EEG signals with a reference fragment pre-selected by medical experts. In the motion artifact detection algorithm, we propose to calculate the mobility index from blurred edge maps of the region of interest, which are less sensitive to fluctuations in illumination level. As an indicator of mobility, we use the variation of information, which characterizes the informational difference of video frames. We obtained preliminary results that confirm the effectiveness of the proposed algorithm. The developed algorithm for early automated diagnostics of delayed cerebral ischemia provides sensitivity equal to 0.9 and specificity equal to 0.79. The obtained quality measures correspond to the quality level of known methods for diagnosing delayed cerebral ischemia based on EEG signals. At the same time, the accuracy of detecting motion artifacts is 0.9, and the F1 score is 0.93. The detection algorithm for epileptiform discharges provides sensitivity of 0.95 and specificity of 0.85.

Source: The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences

DOI: 10.5194/isprs-archives-xlviii-2-w9-2025-195-2025

Authors: Dmitry Murashov, Yury Obukhov, Ivan Kershner, Mikhail Sinkin, Irina Okuneva

Institutions: Russian Academy of Sciences, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences, N.V. Sklifosovsky Research Institute of Emergency Medicine

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: Russian Science Foundation