An Evolving Cascade Neural Network Technique for Cleaning Sleep Electroencephalograms

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dc.creatorSchetinin, Vitaly
dc.date2005-04-14
dc.date.accessioned2026-07-07T03:22:52Z
dc.date.available2026-07-07T03:22:52Z
dc.descriptionEvolving Cascade Neural Networks (ECNNs) and a new training algorithm capable of selecting informative features are described. The ECNN initially learns with one input node and then evolves by adding new inputs as well as new hidden neurons. The resultant ECNN has a near minimal number of hidden neurons and inputs. The algorithm is successfully used for training ECNN to recognise artefacts in sleep electroencephalograms (EEGs) which were visually labelled by EEG-viewers. In our experiments, the ECNN outperforms the standard neural-network as well as evolutionary techniques.
dc.identifierhttps://arxiv.org/abs/cs/0504067
dc.identifierhttp://arxiv.org/abs/cs/0504067
dc.identifierNatural Computing Application, 2005
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32724
dc.subjectNeural and Evolutionary Computing
dc.subjectArtificial Intelligence
dc.titleAn Evolving Cascade Neural Network Technique for Cleaning Sleep Electroencephalograms
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