FIELD: medicine.
SUBSTANCE: invention refers to medicine, namely to clinical medicine - neurology and neurosurgery. Disclosed is a method for predicting the course of an acute period of hemorrhagic parenchymal insult of supratentorial localization based on neuroimaging data, electroencephalography, endogenous evoked P300 potentials and heart rate variability using machine learning techniques. In a patient with diagnosed parenchymal haemorrhagic stroke of supratentorial localization according to X-ray computed tomography, volume of hematoma is determined, localization (lobar; medial and lateral subcortical), localization of hematoma (lateralisation); electroencephalogram is recorded for 30 minutes, endogenous evoked potentials on auditory stimulus (potential P300), heart rate variability is assessed.
EFFECT: invention provides an effective method for predicting the course of an acute period of hemorrhagic parenchymal insult of supratentorial localization.
1 cl, 1 dwg, 6 tbl, 2 ex
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