  Objective: Epileptic seizures are relatively common in critically-ill
children admitted to the pediatric intensive care unit (PICU) and thus serve as
an important target for identification and treatment. Most of these seizures
have no discernible clinical manifestation but still have a significant impact
on morbidity and mortality. Children that are deemed at risk for seizures
within the PICU are monitored using continuous-electroencephalogram (cEEG).
cEEG monitoring cost is considerable and as the number of available machines is
always limited, clinicians need to resort to triaging patients according to
perceived risk in order to allocate resources. This research aims to develop a
computer aided tool to improve seizures risk assessment in critically-ill
children, using an ubiquitously recorded signal in the PICU, namely the
electrocardiogram (ECG). Approach: A novel data-driven model was developed at a
patient-level approach, based on features extracted from the first hour of ECG
recording and the clinical data of the patient. Main results: The most
predictive features were the age of the patient, the brain injury as coma
etiology and the QRS area. For patients without any prior clinical data, using
one hour of ECG recording, the classification performance of the random forest
classifier reached an area under the receiver operating characteristic curve
(AUROC) score of 0.84. When combining ECG features with the patients clinical
history, the AUROC reached 0.87. Significance: Taking a real clinical scenario,
we estimated that our clinical decision support triage tool can improve the
positive predictive value by more than 59% over the clinical standard.
