RT Journal Article
T1 Non-linear classifiers applied to EEG analysis for epilepsy seizure detection
A1 Martinez-Del-Rincon, Jesus
A1 Santofimia, Maria J.
A1 del Toro, Xavier
A1 Barba, Jesus
A1 Romero, Francisca
A1 Navas, Patricia
A1 Lopez, Juan C.
K1 Classification algorithms
K1 Non-linear classifiers
K1 SVM
K1 Bag of words
K1 Wavelet
K1 Epilepsy
K1 Support vector machines
K1 Neural-networks
K1 Signals
K1 Classification
K1 Identification
K1 Mixture
AB This work presents a novel approach for automatic epilepsy seizure detection based on EEG analysis that exploits the underlying non-linear nature of EEG data. In this paper, two main contributions are presented and validated: the use of non-linear classifiers through the so-called kernel trick and the proposal of a Bag-of-Words model for extracting a non-linear feature representation of the input data in an unsupervised manner. The performance of the resulting system is validated with public datasets, previously processed to remove artifacts or external disturbances, but also with private datasets recorded under realistic and non-ideal operating conditions. The use of public datasets caters for comparison purposes whereas the private one shows the performance of the system under realistic circumstances of noise, artifacts, and signals of different amplitudes. Moreover, the proposed solution has been compared to state-of-the-art works not only for pre-processed and public datasets but also with the private datasets. The mean F1-measure shows a 10% improvement over the second-best ranked method including cross-dataset experiments. The obtained results prove the robustness of the proposed solution to more realistic and variable conditions. (C) 2017 Elsevier Ltd. All rights reserved.
PB Pergamon-elsevier science ltd
SN 0957-4174
YR 2017
FD 2017-11-15
LK http://hdl.handle.net/10668/18772
UL http://hdl.handle.net/10668/18772
LA en
DS RISalud
RD Apr 4, 2025