Algorithm for detecting epileptiform EEG activity in delayed cerebral ischemia

Abstract

An algorithm for automatic detection of epileptiform activity in EEG monitoring data with delayed ischemia after hemorrhage in the subarachnoid space of the brain is proposed and described. The algorithm is based on the formalization of the visual characteristics of epileptiform activity in the form of a peak-wave discharge pattern and analysis of the mutual correlation of multichannel EEG signals with the selected pattern. Fragments of epileptiform activity in each pair of bipolar electrodes were determined from three conditions according to the description of peak-wave discharges of epileptiform activity: 1) the value of positive mutual correlation at the peak of the correlation function should be greater than 0.4; 2) a positive peak of mutual correlation should be followed by a peak with a negative correlation; 3) the width of the peak of negative mutual correlation at half-altitude should be at least 2 times greater than that of the previous positive peak of positive mutual correlation. As with the visual detection of epileptiform activity, the neurophysiologist selected simultaneous peak-wave discharges in several bipolar electrodes. The results of testing the algorithm on an hour-long EEG recording of a patient with delayed ischemia are presented. Fragments with epileptiform activity during the hour under review were identified 17 in the right hemisphere and 2 in the left. Interhemispheric asymmetry is caused by a right-sided aneurysm in patient. The operating time of the algorithm on a modern personal computer is no more than 5 minutes to process 16 bipolar signals, so it can be used to calculate the hourly amount of epileptiform activity in almost real time of the manifestation of this indicator of delayed ischemia after aneurysmal subarachnoid hemorrhage.