Predicting the neurodevelopmental outcome in newborns with hypoxic-ischaemic injury

Abstract

The prediction of outcome in newborns with hypoxic ischemic encephalopathy (HIE) is a problematic task. Here, the ability of a combination of clinical, heart rate and EEG measures to predict outcome at 2 years is investigated. One hour of EEG and ECG recordings were obtained from newborns 24 hours after birth. Each newborn was reassessed at 24 months to investigate their neurodevelopmental outcome. From the EEG and ECG recordings, a set of 12 features was extracted. To classify each baby's outcome this data, along with clinical information was fed to a support vector machine. On a per patient basis an ROC area of 0.768 was achieved with 73.68% of newborns being assigned the correct outcome. Overall, this system presents a promising step towards the use of multimodal data for the prediction of neurodevelopmental outcome in newborns with HIE.