Epilepsy has been reported in 10-40% of children in the paediatric intensive care unit (PICU). Amplitude-integrated electroencephalography (aEEG), often used as neuromonitoring in the PICU, has some limitations and as a result, caretakers in the PICU may find it challenging to interpret aEEG. This may lead to uncertainty during diagnosis and increases the need for the assistance of the neurophysiology department, a costly EEG and a late diagnosis which might result in irreversible harm.
The aim of this exploratory study was to identify and evaluate observer-based features as stand-alone classifiers and combined in a random forest classifier, that could aid in the accurate classification of seizures in paediatric critical care patients with 4-electrode EEG.
Several features achieved an AUC above 0.60, with the lower border of the aEEG signal yielding the highest AUC of 0.79. The best-performing features maintained their classifying ability when tested on a larger, independent dataset. The random forest classifier did not provide better results.
Our results show that various observer-based features can aid in the identification of seizures in 4-electrode EEG. These additional features could increase the accuracy and decrease the delay and uncertainty in diagnosing epilepsy in aEEG by PICU staff, reducing the need for full setup EEG monitoring and improving patient outcomes in paediatric critical care.