AN ALGORITHM FOR AUTOMATED DETECTION OF DELAYED BRAIN ISCHEMIA INDICATOR FROM VIDEO-EEG MONITORING DATA

Abstract

In this work, we propose a new algorithm for detecting the indicator of delayed cerebral ischemia from video-EEG monitoring data. The proposed algorithm combines an algorithm for detecting the effect of interchannel time-frequency synchronization of wavelet spectrogram ridges of EEG signals and an algorithm for detecting motion artifacts in video recording frames. Using the EEG analysis algorithm, we identify an indicator that is used to predict the occurrence of ischemia. By analyzing the optical flow, we exclude time intervals in which erroneous fixation of ischemia indicators is possible. The developed algorithm was tested on the clinical data of video-EEG monitoring. The preliminary results obtained during testing confirm the fundamental possibility of detecting the ischemia indicators and the acceptable accuracy of detecting motion artifacts leading to an erroneous diagnosis.