  Evolving 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.
