Publications

My two papers presented on NIMIA-2001 workshop
are available on http://nnlab.tripod.com under Online Publications.
Any feedback is welcome!
Vitaly Schetinin

A Neural Network Decision Tree for Learning Concepts from EEG Data
Vitaly Schetinin
Abstract - 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 algorithm. We found
that the known methods fail inducting the classification models when the
data are presented by the features some of them are irrelevant, and the
classes are heavily overlapped. To train the DT, our algorithm exploits a
bottom up search of the features that provide the best classification
accuracy of the linear tests. We applied the developed algorithm to induce
the DT from the large EEG dataset consisted of 65 patients belonging to 16
age groups. In these recordings each EEG segment was represented by 72
calculated features. The DT correctly classified 80.8% of the training and
80.1% of the testing examples. Correspondingly it correctly classified 89.2%
and 87.7% of the EEG recordings.

Polynomial Neural Networks Learnt to Classify EEG Signals
Vitaly Schetinin
Abstract - A neural network based technique is presented, which is able to
successfully extract polynomial classification rules from labeled
electroencephalogram (EEG) signals. To represent the classification rules in
an analytical form, we use the polynomial neural networks trained by a
modified Group Method of Data Handling (GMDH). The classification rules were
extracted from clinical EEG data that were recorded from an Alzheimer
patient and the sudden death risk patients. The third data is EEG recordings
that include the normal and artifact segments. These EEG data were visually
identified by medical experts. The extracted polynomial rules verified on
the testing EEG data allow to correctly classify 72% of the risk group
patients and 96.5% of the segments. These rules performs slightly better
than standard feed-forward neural networks.

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  Dr. Vitaly Schetinin,  Tel: x49-03641-949534
E-mail b0scvi@uni-jena.de
www http://nnlab.tripod.com
TheorieLabor http://www.theorielabor.de
  Friedrich-Schiller-Universit�t, Ernst-Abbe-Platz 4
  D-07740 Jena, Germany
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