An algorithm and programs have been developed in the MATLAB for automatic detection of electroencephalographic predictors of delayed cerebral ischemia after non-traumatic subarachnoid hemorrhage. These predictors are: (1) a decrease in the ratio of the maximum spectral power density of electroencephalogram rhythms in the delta and alpha frequency bands, (2) a decrease in the relative spectral power density of the rhythm in the alpha range, and (3) an increase in the number of sporadic or generalized epileptiform discharges. Predictors (1) and (2) are calculated from 3-hour electroencephalogram spectra, and predictor (3) is calculated from the functions of cross-correlation of electroencephalograms with a selected epileptiform discharge sample. Unlike the well-known automated algorithms for retrospective detection of predictors, which require the participation of neurophysiologists to select artifact-free areas, the developed algorithms make it possible to detect predictors automatically in the presence of artifacts and without the participation of neurophysiologists. Algorithms and programs were tested at individual long-term electroencephalographic monitoring data of 25 patients with suspected development of delayed cerebral ischemia after subarachnoid hemorrhage from the Intensive Care Unit of N.V. Sklifosovsky Research Institute of Emergency Medicine. The execution time of programs on a modern personal computer is several minutes, it makes possible to detect predictors of delayed cerebral ischemia in real time of their manifestation and inform the attending physician about this to make a decision on intensifying the patient's treatment.
Obukhov et al. (2025) studied this question.