A New Wavelet-Based Neural Network for Classification of Epileptic-Related States using EEG
| Autor: | Pilar Gomez-Gil, Vicente Alarcon-Aquino, Juan Manuel Ramirez-Cortes, E.S. Garcia-Trevino, E. Juárez-Guerra |
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| Rok vydání: | 2019 |
| Předmět: |
Discrete wavelet transform
Finite impulse response Artificial neural network business.industry Computer science 020206 networking & telecommunications Pattern recognition 02 engineering and technology Cross-validation Theoretical Computer Science Wavelet Binary classification Hardware and Architecture Control and Systems Engineering Robustness (computer science) Modeling and Simulation Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Infinite impulse response Information Systems |
| Zdroj: | Journal of Signal Processing Systems. 92:187-211 |
| ISSN: | 1939-8115 1939-8018 |
| DOI: | 10.1007/s11265-019-01456-7 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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