FIELD: computing technology.
SUBSTANCE: invention relates to medicine and computing technology. The method comprises the stages wherein at least one EEG signal is preprocessed on a computing device, where local extrema with a window of 0.02 seconds are identified in at least one EEG signal, wherein each extremum is marked in accordance with the available markup; indicators are calculated for each extremum based on the original EEG signal and the wavelet transformation of the original EEG signal; a sample of indicators and marked marks is received by the input of the classifier designed for assessment of the probability of presence of epileptiform patterns in a signal segment; to reduce the number of false negatives, the probability threshold is determined as equal to 5%; post-processing of the obtained results of the classifier is conducted, close extrema marked as epileptiform are combined into one epileptiform discharge.
EFFECT: ensured accurate detection of generalised epileptiform discharges, reduced number of skipped discharges and reduced time for detection of generalised epileptiform discharges.
2 cl, 3 dwg, 3 tbl
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Authors
Dates
2021-05-13—Published
2020-06-01—Filed