16 citations · 45 across the 17 of their papers we have counts for
11 papers · 1 filter
The Combined Technique for Detection of Artifacts in Clinical Electroencephalograms of Sleeping Newborns
Vitaly Schetinin, Joachim Schult
In this paper we describe a new method combining the polynomial neural network and decision tree techniques in order to derive comprehensible classification rules from clinical ele…
A Neural-Network Technique to Learn Concepts from Electroencephalograms
Vitaly Schetinin, Joachim Schult
A new technique is presented developed to learn multi-class concepts from clinical electroencephalograms. A desired concept is represented as a neuronal computational model consist…
Self-Organization of the Neuron Collective of Optimal Complexity
V. Schetinin, A. Kostunin
The optimal complexity of neural networks is achieved when the self-organization principles is used to eliminate the contradictions existing in accordance with the K. Godel theorem…
An Evolving Cascade Neural Network Technique for Cleaning Sleep Electroencephalograms
Vitaly Schetinin
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 the…
A Neural Network Decision Tree for Learning Concepts from EEG Data
Vitaly Schetinin
To learn the multi-class conceptions from the electroencephalogram (EEG) data we developed a neural network decision tree (DT), that performs the linear tests, and a new training a…
Polynomial Neural Networks Learnt to Classify EEG Signals
Vitaly Schetinin
A neural network based technique is presented, which is able to successfully extract polynomial classification rules from labeled electroencephalogram (EEG) signals. To represent t…