Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients
Authors
César Teixeira
Bruno Miguel Direito Pereira Leitão
Mojtaba Bandarabadi
Michel LeVanQuyen
Mário Valderrama
Bjoern Schelter
Andreas Schulze Bonhage
Vincent Navarro
Francisco Sales
António Dourado
Bruno Miguel Direito Pereira Leitão
Mojtaba Bandarabadi
Michel LeVanQuyen
Mário Valderrama
Bjoern Schelter
Andreas Schulze Bonhage
Vincent Navarro
Francisco Sales
António Dourado
Keywords
Epileptic Seizure Prediction, Artificial Neural Networks, Support Vector Machines, EPILEPSIAE project, European Epilepsy DatabaseRelated Project
EPILEPSIAE- Evolving Platform for Improving Living Expectation of PatientsJournal
Computer Methods and Programs in Biomedicine, February 2014DOI
Cited by
Year 2016 : 5 citations
Zhang, Z. and Parhi, K.K., 2016. Low-complexity seizure prediction from iEEG/sEEG using spectral power and ratios of spectral power. IEEE transactions on biomedical circuits and systems, 10(3), pp.693-706.
Brinkmann, B.H., Wagenaar, J., Abbot, D., Adkins, P., Bosshard, S.C., Chen, M., Tieng, Q.M., He, J., Muñoz-Almaraz, F.J., Botella-Rocamora, P. and Pardo, J., 2016. Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain, 139(6), pp.1713-1722.
Lehnertz, K., Dickten, H., Porz, S., Helmstaedter, C. and Elger, C.E., 2016. Predictability of uncontrollable multifocal seizures–towards new treatment options. Scientific reports, 6.
Ulate-Campos, A., Coughlin, F., Gaínza-Lein, M., Fernández, I.S., Pearl, P.L. and Loddenkemper, T., 2016. Automated seizure detection systems and their effectiveness for each type of seizure. Seizure, 40, pp.88-101.
Satapathy, S.K., Dehuri, S. and Jagadev, A.K., 2016. An Empirical Analysis of Different Machine Learning Techniques for Classification of EEG Signal to Detect Epileptic Seizure. International Journal of Applied Engineering Research, 11(1), pp.120-129.
Year 2015 : 3 citations
Seizure prediction using polynomial SVM classification
Z Zhang, KK Parhi - … in Medicine and Biology Society (EMBC), …, 2015 - ieeexplore.ieee.org
Abstract—This paper presents a novel patient-specific algorithm for prediction of seizures in
epileptic patients with low hardware complexity and low power consumption. In the
proposed approach, we first compute the spectrogram of the input fragmented EEG ...
Low-Complexity Seizure Prediction From iEEG/sEEG Using Spectral Power and Ratios of Spectral Power
Z Zhang, KK Parhi - 2015 - ieeexplore.ieee.org
EEG patterns are not wide-sense stationary and change from seizure to seizure, electrode to
electrode, and from patient to patient. This paper presents a novel patient-specific algorithm
for prediction of seizures in epileptic patients from either one or two single-channel or ...
Transcranial Magnetic Stimulation Combined with EEG Reveals Covert States of Elevated Excitability in the Human Epileptic Brain
VK Kimiskidis, C Koutlis, A Tsimpiris… - … journal of neural …, 2015 - World Scientific
Background: Transcranial magnetic stimulation combined with electroencephalogram (TMS-
EEG) can be used to explore the dynamical state of neuronal networks. In patients with
epilepsy, TMS can induce epileptiform discharges (EDs) with a stochastic occurrence ...