Automated EEG Analysis for Neonatal Intensive Care

Abstract The neonatal EEG is a powerful tool for assessing neurological function in critically ill neonates. It is however difficult to interpret and the expertise to translate the EEG is typically not available to the attending clinician on demand. Automated algorithms can fill the role of EEG translator and help support decision making in the neonatal intensive care unit. These algorithms can detect seizures, bursting activity and sleep state, determine the ‘normality’ of the EEG, and estimate the developmental maturity of the brain. They use a variety of EEG features combined using a range of classifiers to form a decision statistic. Several of these algorithms are approaching the benchmark of the visual interpretation of the human expert, while others are at the prototype stage. The complete development of these algorithms requires the collective efforts of engineers and clinicians and has the potential to improve clinical care and subsequent health outcomes of critically ill neonates.