Quantifying and visualizing uncertainty in EEG data of neonatal seizures.
Conf Proc IEEE Eng Med Biol Soc
; 2006: 423-6, 2004.
Article
em En
| MEDLINE
| ID: mdl-17271702
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.
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Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2004
Tipo de documento:
Article