A New Wavelet-Based Neural Network for Classification of Epileptic-Related States using EEG

  • E. Juárez-Guerra
  • V. Alarcon-Aquino
  • P. Gómez-GilEmail author
  • J. M. Ramírez-Cortés
  • E. S. García-Treviño


In this paper, we present a novel neural network able to classify epileptic seizures using electroencephalogram (EEG) signals, called “Multidimensional Radial Wavelons Feed-Forward Wavelet Neural Network” (MRW-FFWNN). The network is part of a classification system, which distinguishes among three brain states related to epilepsy namely ictal, interictal and healthy. Efficient methods for pre-processing EEG’s, extracting features and getting the final class decisions were selected using a statistical three-fold cross-validation method, which assures the robustness of the system and its generalization ability. The following methods were systematically analyzed to find the most appropriate for this problem: 1) Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) filters for noise reduction; 2) discrete Wavelet Transform (DWT) and Maximal Overlap Discrete Wavelet Transform (MODWT) for frequency decomposition of the EEG signals; 3) average correlation and maximum voting correlation for selecting a suitable mother wavelet for frequency decomposition; 4) Binary-tree and one-vs-one (OVO) decomposition strategies for primary binary classification; 5) voting and weighted-voting strategy aggregation strategies for the final classification. The integrated system was assessed using a three-fold cross validation, applied to a benchmark provided by the University of Bonn, getting an average accuracy of 93.33% when tested using sets Z, S and F and 95.0% when sets Z, S, F and O were used. The proposed network got competitive accuracy, compared with other state-of-the art classifiers, training in almost a half of the time than the ones with similar accuracy.


Wavelet-based neural networks Epileptic seizure detection EEG analysis Machine learning classification Wavelet selection 



The first author gratefully acknowledges the financial support from the Universidad Autónoma de Tlaxcala and the Teacher Improvement Program (PROMEP) by scholarship No. UATLX-244. This research was partially supported by the National Council of Science and Technology in México (CONACYT), project grant No. CB-2010-155250.


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Authors and Affiliations

  1. 1.Universidad Autónoma de TlaxcalaTlaxcalaMexico
  2. 2.Universidad de las AméricasPueblaMexico
  3. 3.Instituto Nacional de Astrofísica Óptica y ElectrónicaPueblaMexico
  4. 4.Instituto de Investigaciones en Ecosistemas y SustentabilidadUNAMMoreliaMéxico

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