Improvement of an automated neonatal seizure detector using a post-processing technique

Visual recognition of neonatal seizures during continuous EEG monitoring in neonatal intensive care units (NICUs) is labor-intensive, has low inter-rater agreement and requires special expertise that is not available around the clock. Development of an accurate automated seizure detection system with a low false alarm rate will support clinical decision making and alleviate significantly the workload. However, this is an ongoing difficult challenge for engineers as the neonatal EEG signal is non-stationary and often includes complex patterns of seizures and artifacts. In this study, we show an improvement of our previously developed neonatal seizure detector (developed using heuristic if-then rules). In order to improve the detection accuracy, mean phase coherence as a new feature is used to characterize artifacts and also support vector machine is applied to perform the post-processing step to remove false detections. As a result, the false alarm rate drops 42% (from 2.6 h^-1 to 1.5 h^-1), whereas the good detection rate reduces only by 4%.