The study aims to demonstrate that the newly developed method of automated EEG analysis can detect abnormal phenomena in the neonatalEEG and can quantify the severity of the deviation from the norm. EEG records from 36 neonates with clinically indicated EEG examination were analyzed. Five-minutes samples of EEG activity during a standardized behavioral state were processed using a computer-supported four-stage analysis and described with 312 numerical items, providing the information on amplitude, power in five frequency bands, on signal shape and its stability or variability. Each of these items was automatically compared to normative data acquired from the examination of 21 healthy, full term neonates and tested whether it differs by one or two standard deviations from the norm. The number of patient’s items differing from the norm was proportional to the severity of their abnormality. A cursory comparison of the results of automatic and visual analyses of patient’ EEG records showed good agreement. The method shows promise for the clinical practice, where it can significantly contribute to both acceleration and greater objectivity of neonatal EEG assessment.

        Key words: neonate, abnormal electroencephalogram, automated EEG analysis