Quantifying and visualizing uncertainty in EEG data of neonatal seizures

N B Karayiannis; A Mukherjee; J R Glover; P Y Ktonas; J D Frost Jr; R A Hrachovy; E M Mizrahi

doi:10.1109/IEMBS.2004.1403184

Quantifying and visualizing uncertainty in EEG data of neonatal seizures

Conf Proc IEEE Eng Med Biol Soc. 2004:2006:423-6. doi: 10.1109/IEMBS.2004.1403184.

Authors

N B Karayiannis¹, A Mukherjee, J R Glover, P Y Ktonas, J D Frost Jr, R A Hrachovy, E M Mizrahi

Affiliation

¹ Dept. of Electr. & Comput. Eng., Houston Univ., TX, USA.

PMID: 17271702
DOI: 10.1109/IEMBS.2004.1403184

Abstract

This paper 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.