Quantifying and visualizing uncertainty in EEG data of neonatal seizures

This work presents an approach to quantifying and visualizing uncertainty in EEG data of neonatal seizures. This approach exploits the inherent ability of trained quantum neural networks (QNNs) to learn arbitrary membership profiles from sample data. The ability of QNNs to quantify uncertainty in data is combined with the ability of ordered self-organizing maps (SOMs) to recognize structure in data and allow its visualization in two dimensions. The proposed approach is evaluated using EEG data of neonates monitored for seizures.