| Título |
Windowed electroencephalographic signal classifier based on continuous neural networks with delays in the input |
| Tipo |
Revista |
| Sub-tipo |
JCR |
| Descripción |
Expert Systems with Applications |
| Resumen |
This study reports the design and implementation of a pattern recognition algorithm aimed to classify electroencephalographic (EEG) signals based on a class of dynamic neural networks (NN) described by time delay differential equations (TDNN). This kind of NN introduces the signal windowing process used in different pattern classification methods. The development of the classifier included a new set of learning laws that considered the impact of delayed information on the classifier structure. Both, the training and the validation processes were completely designed and evaluated in this study. The training method for this kind of NN was obtained by applying the Lyapunov theory stability analysis. The accuracy of training process was characterized in terms of the number of delays. A parallel structure (similar to an associative memory) with fixed (obtained after training) weights was used to execute the validation stage. Two methods were considered to validate the pattern classification method: a generalization-regularization and the k-fold cross validation processes (k = 5). Two different classes were considered: normal EEG and patients with previous confirmed neurological diagnosis. The first one contains the EEG signals from 100 healthy patients while the second contains information of epileptic seizures from the same number of patients. The pattern classification algorithm achieved a correct classification percentage of 92.12% using the information of the entire database. |
| Observaciones |
http://dx.doi.org/10.1016/j.eswa.2016.08.020 |
| Lugar |
|
| País |
Reino Unido |
| No. de páginas |
1–10 |
| Vol. / Cap. |
68 |
| Inicio |
2017-02-24 |
| Fin |
|
| ISBN/ISSN |
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