Automatic electroencephalographic information classifier based on recurrent neural networks
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ORIGINAL ARTICLE
Automatic electroencephalographic information classifier based on recurrent neural networks Mariel Alfaro‑Ponce1,3 · Amadeo Argüelles2 · Isaac Chairez3 · Arizbeth Pérez4 Received: 14 December 2017 / Accepted: 21 August 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract The aim of this study was to design an automatic classifier for electroencephalographic information (EEGI) registered in evoked potentials experiments. The classifier used a parallel associative memory based on recurrent neural networks (RNNs). Each RNN was trained to classify signals belonging to an individual class. A recurrent method based on the application of Lyapunov controlled functions served to design the training procedure of each RNN in the classifier. A parallel structure of RNN with fixed weights (obtained after training process) performed the validation stage. This structure formed a classifier assemble. The selected class assigned to a new segment of EEGI signal is estimated by the minimum value of the least mean square error among the RNNs forming the assemble. The generalization-regularization and a k-fold cross validation ( k = 5 ) were the validation methods evaluating the classifier efficiency. The confusion matrix method justified the application of the classification method introduced in this study. The EEGI obtained from two different annotated databases served to test the classifier based on RNNs. The first database contained signals divided in five different classes and collected from patients suffering from epilepsy. The second database has 90 signals divided in three classes that corresponded to EEGI signals corresponding to 3 different visual evoked potentials. The pattern classifier achieved a maximum correct classification percentage of 97.2% using the information of both databases. This value prevailed over results reported in similar studies using the first database. In comparison with other pattern recognition algorithms, the proposed RNNs based classifier attained similar or even better correct classification results. Keywords Recurrent neural networks · Electroencephalographic information · Biosignal automatic classifier · Classifier assemble · Parallel associative method
1 Introduction
* Mariel Alfaro‑Ponce [email protected] 1
Departamento de Ciencias e Ingeniería, Universidad Iberoamericana Puebla, Blvd. del Niño Poblano 2901, Reserva Territorial Atlixcáyotl, C.P. 72810 San Andrés Cholula, Pue., Mexico
2
Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz Esq. Miguel Othón de Mendizábal S/N, 07738 Gustavo A. Madero, CDMX, Mexico
3
Unidad Profesional Interdisciplinaría de Biotecnología, Instituto Politécnico Nacional, Av. Acueducto S/N col. Barrio La Laguna Ticoman, Mexico, Mexico
4
Facultad de Ciencias Químicas, Universidad Autónoma de Chihuahua, Circuito Universitario #2, Campus UACH II, C.P. 31125 Chihuahua, Ch., Mexico
The current increasing popularity of artificial neural networks (NNs) is a conseq
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