A New Wavelet-Based Neural Network for Classification of Epileptic-Related States using EEG. Juárez-Guerra, E., Alarcon-Aquino, V., Gómez-Gil, P., Ramírez-Cortés, J., M., & García-Treviño, E., S. Journal of Signal Processing Systems, 92(2):187-211, 2, 2020. ![A New Wavelet-Based Neural Network for Classification of Epileptic-Related States using EEG [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 3 downloads 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.
@article{
title = {A New Wavelet-Based Neural Network for Classification of Epileptic-Related States using EEG},
type = {article},
year = {2020},
keywords = {EEG analysis,Epileptic seizure detection,Machine learning classification,Wavelet selection,Wavelet-based neural networks},
pages = {187-211},
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websites = {http://link.springer.com/10.1007/s11265-019-01456-7},
month = {2},
day = {17},
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abstract = {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.},
bibtype = {article},
author = {Juárez-Guerra, E. and Alarcon-Aquino, V. and Gómez-Gil, P. and Ramírez-Cortés, J. M. and García-Treviño, E. S.},
doi = {10.1007/s11265-019-01456-7},
journal = {Journal of Signal Processing Systems},
number = {2}
}
Downloads: 3
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