EEG classification is a research topic that has attracted a lot of interest in recent years, as proven by the large number of papers published. To accomplish this task, a lot of classification systems such as Support Vector Machines (SVMs) or Artificial Neural Networks (ANNs) are used. However, Recurrent Artificial Neural Networks (RANNs) that allow using the previously computed results to generate the actual output have hardly been used, although intuitively they may seem to be very useful in this field. This article proposes the use of RANNs to solve a well-known problem: the detection of epileptic seizures in EEG signals. The results show that RANNs can work it out satisfactorily, with a higher accuracy than other techniques previously used.
['Daniel Rivero', 'Enrique Fernández-Blanco', 'Julian Dorado', 'Alejandro Pazos']
Using recurrent ANNs for the detection of epileptic seizures in EEG signals