Influence of human-expert labels on a neonatal seizure detector based on a convolutional neural network
Ana Borovac, Thomas P Runarsson, Steinn Guðmundsson, Gardar Thorvardsson
Neonatal seizures are common among infants and can be detected with an electroencephalogram (EEG). The EEG signals are complex time-series using multiple channels. Human domain experts are often in disagreement when labelling neonatal seizure data. Only few studies will include labels from multiple experts, as annotating hours of EEG recordings is time consuming and expensive. In this study we investigate the differences in performance of a deep-learning-based neonatal seizure detector trained using single expert labelling versus data labelled using the consensus of multiple experts. Results indicate that there are differences even when the experts are in minor disagreement. We find that excluding ambiguously labeled data is important when training a neonatal seizure detector.