Estimating the Parameters of the Epileptor Model for Epileptic Seizure Suppression

Abuhasel, K. A., Iliyasu, A. M., & Fatichah, C. (2015). A hybrid particle swarm optimization and neural network with fuzzy membership function technique for epileptic seizure classification. Journal of Advanced Computational Intelligence and Intelligent Informatics, 19(3), 447–455.

Google Scholar 

Agarwal, S., Rani, A., Singh, V., & Mittal, A. P. (2017). EEG signal enhancement using cascaded S-Golay filter. Biomedical Signal Processing and Control, 36, 194–204.

Google Scholar 

Altan, A. (2020). Performance of metaheuristic optimization algorithms based on swarm intelligence in attitude and altitude control of unmanned aerial vehicle for path following. In: 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1–6.) IEEE

Altan, A & Parlak, A. (2020). Adaptive control of a 3D printer using whale optimization algorithm for bio-printing of artificial tissues and organs. In: Innovations in Intelligent Systems and Applications Conference (ASYU) (pp.  pp. 1–5). IEEE.

Boyd, S., El Ghaoui, L., & Feron, E. (1994). & Balakrishnan (Vol. Linear). SIAM-Philadelphia, Pennsylvania: Matrix Inequalities in System and Control Theory.

Google Scholar 

Brogin, J. A. F., Faber, J., & Bueno, D. D. (2020). An efficient approach to define the input stimuli to suppress epileptic seizures described by the epileptor model. Journal of Neural Systems, 2050062.

Brogin, J. A. F., Faber, J., & Bueno, D. D. (2021). Burster reconstruction considering unmeasurable variables in the Epileptor model. Neural Computation, 33(12), 3288–3333.

PubMed  Google Scholar 

Browne, T. R., & Holmes, G. L. (2008). Handbook of epilepsy. Pennsylvania: Lippincott Williams & Wilkins-Philadelphia.

Google Scholar 

Chen, X., Liu, A., Chiang, J., Wang, Z. J., McKeown, M. J., & Ward, R. K. (2015). Removing muscle artifacts from EEG data: Multichannel or single-channel techniques? IEEE Sensors Journal, 16(7), 1986–1997.

Google Scholar 

Chizhov, A. V., Zefirov, A. V., Amakhin, D. V., Smirnova, E. Y., & Zaitsev, A. V. (2018). Minimal model of interictal and ictal discharges epileptor-2. PLOS Computational Biology, 14(5), 1–25.

Google Scholar 

Cota, V. R., de Castro Medeiros, D., da Páscoa Vilela, M. R. S., Doretto, M. C., & Moraes, M. F. D. (2009). Distinct patterns of electrical stimulation of the basolateral amygdala influence pentylenetetrazole seizure outcome. Epilepsy & Behavior, 14(1), 26–31.

Google Scholar 

D’Andrea Meira, I., Romão, T. T., Pires do Prado, H. J., Krüger, L. T., Pires, M. E. P. & da Conceição, P. O. (2019). Ketogenic diet and epilepsy: what we know so far. Frontiers in Neuroscience-Switz, 13, 5.

Dollfuss, P., Hartmann, M. M., Skupch, A., Frbass, F., & Kluge, T. (2013). Automatic optimization of parameters for seizure detection systems. In: 5th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1976–1979). IEE,.

El Houssaini, K., Ivanov, A. I., Bernard, C., & Jirsa, V. K. (2015). Seizures, refractory status epilepticus, and depolarization block as endogenous brain activities. Physical Review E, 91, 010701.

Fisher, R. S., & Schachter, S. C. (2000). The postictal state: a neglected entity in the management of epilepsy. Epilepsy & Behavior, 1(1), 52–59.

CAS  Google Scholar 

FitzHugh, R. (1961). Impulses and physiological states in theoretical models of nerve membrane. Biophysical Journal, 1(6), 445–466.

CAS  PubMed  PubMed Central  Google Scholar 

Gao, F., & Han, L. (2012). Implementing the Nelder-Mead simplex algorithm with adaptive parameters. Computational Optimization and Applications, 51(1), 259–277.

Google Scholar 

Grimbert, F., & Faugeras, O. (2006). Bifurcation analysis of Jansen’s neural mass model. Neural Computation, 18(12), 3052–3068.

PubMed  Google Scholar 

Hamad, A., Houssein, E. H., Hassanien, A. E., & Fahmy, A. A. (2018). Hybrid grasshopper optimization algorithm and support vector machines for automatic seizure detection in EEG signals. In: International conference on advanced machine learning technologies and applications (pp. 82–91). Springer, Cham

Hardt, M., Schraknepper, D., & Bergs, T. (2021). Investigations on the application of the downhill-simplex-algorithm to the inverse determination of material model parameters for FE-machining simulations. Simulation Modelling Practice and Theory, 107, 102214.

Hashemi, M., Vattikonda, A. N., Sip, V., Guye, M., Bartolomei, F., Woodman, M. M., & Jirsa, V. K. (2020). The Bayesian Virtual Epileptic Patient: A probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread. NeuroImage, 217, 116839.

Hawkins, D. M. (2004). The problem of overfitting. Journal of chemical information and computer sciences, 44(1), 1–12.

CAS  PubMed  Google Scholar 

Hindmarsh, J. L., & Rose, R. M. (1984). A model of neuronal bursting using three coupled first order differential equations. Proceedings of the Royal Society B: Biological Sciences, 221(1222), 87–102.

CAS  Google Scholar 

Hodgkin, A. L. & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology London, 117(4), 500–544.

Hussein, R., Elgendi, M., Wang, Z. J., & Ward, R. K. (2018). Robust detection of epileptic seizures based on L1-penalized robust regression of EEG signals. Expert Systems with Applications, 104, 153–167.

Google Scholar 

Iasemidis, L. D. (2003). Epileptic seizure prediction and control. IEEE Transactions on Biomedical Engineering, 50(5), 549–558.

PubMed  Google Scholar 

Ichalal, D., Arioui, H. & Mammar, S. (2011). Observer design for two-wheeled vehicle: A Takagi-Sugeno approach with unmeasurable premise variables. In: 2011 19th Mediterranean Conference on Control & Automation (MED) (pp. 934–939). IEEE.

Islam, M. K., Rastegarnia, A., & Yang, Z. (2015). A wavelet-based artifact reduction from scalp EEG for epileptic seizure detection. IEEE Journal of Biomedical and Health Informatics, 20(5), 1321–1332.

PubMed  Google Scholar 

Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks and Learning Systems, 14(6), 1569–1572.

CAS  Google Scholar 

Jirsa, V. K., Proix, T., Perdikis, D., Woodman, M. M., Wang, H., Gonzalez-Martinez, J., et al. (2017). The virtual epileptic patient: individualized whole-brain models of epilepsy spread. NeuroImage, 145, 377–388.

CAS  PubMed  Google Scholar 

Jirsa, V. K., Stacey, W. C., Quilichini, P. P., Ivanov, A. I., & Bernard, C. (2014). On the nature of seizure dynamics. Brain, 137(8), 2210–2230.

PubMed  PubMed Central  Google Scholar 

Luersen, M. A., & Le Riche, R. (2004). Globalized Nelder-Mead method for engineering optimization. Computers & Structures, 82(23–26), 2251–2260.

Google Scholar 

Kim, H., Bernhardt, B. C., Kulaga-Yoskovitz, J., Caldairou, B., Bernasconi, A., & Bernasconi, N. (2014). Multivariate hippocampal subfield analysis of local MRI intensity and volume: application to temporal lobe epilepsy. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 170–178). Springer, Cham.

Li, Y., Wang, X. D., Luo, M. L., Li, K., Yang, X. F., & Guo, Q. (2017). Epileptic seizure classification of EEGs using time-frequency analysis based multiscale radial basis functions. IEEE Journal of Biomedical and Health Informatics, 22(2), 386–397.

PubMed  Google Scholar 

Liu, W., & Lin, W. (2006). Additive white Gaussian noise level estimation in SVD domain for images. IEEE Transactions on Image Processing., 22(3), 872–883.

Google Scholar 

Ljung, L. (1998). System identification: theory for the user. New Jersey: Prentice Hall.

Lofberg, J. (2011). YALMIP: A toolbox for modeling and optimization in MATLAB. In: 2004 IEEE international conference on robotics and automation (pp. 284–289). IEEE.

Morris, C., & Lecar, H. (1981). Voltage oscillations in the barnacle giant muscle fiber. Biophysical Journal, 35(1), 193–213.

CAS  PubMed  PubMed Central  Google Scholar 

Nagaraj, V., Lamperski, A. & Netoff, T. I. (2017). Seizure control in a computational model using a reinforcement learning stimulation paradigm. International Journal of Neural Systems, 27(07).

Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7(4), 308–313.

Google Scholar 

Neto L. A., Erasme D., Genay N, Chanclou P., Deniel Q., Traore F., Anfray T., Hmadou R. & Aupetit-Berthelemot C. (2012). Simple estimation of fiber dispersion and laser chirp parameters using the downhill simplex fitting algorithm. Journal of Lightwave Technology, 31(2), 334–342.

Pinheiro, D. J., Oliveira, L. F., Souza, I. N., Brogin, J. A. F., Bueno, D. D., Miranda, I. A., et al. (2020). Modulation in phase and frequency of neural oscillations during epileptiform activity induced by neonatal Zika virus infection in mice. Scientific Reports, 10(1), 1–14.

Google Scholar 

Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm Intelligence, 1(1), 33–57.

Google Scholar 

Powell, T. D. (2002). Automated tuning of an extended Kalman filter using the downhill simplex algorithm. Journal of Guidance, Control, and Dynamics, 25(5), 901–908.

Google Scholar 

Proix, T., Bartolomei, F., Guye, M., & Jirsa, V. K. (2017). Individual brain structure and modeling predict seizure propagation. Brain, 140(3), 641–654.

PubMed  PubMed Central  Google Scholar 

Proix, T., Bartolomei, F., Chauvel, P., Bernard, C., & Jirsa, V. K. (2014). Permittivity coupling across brain regions determines seizure recruitment in partial epilepsy. Journal of Neuroscience, 34(45), 15009–15021.

CAS  PubMed  Google Scholar 

Proix, T., Jirsa, V. K., Bartolomei, F., Guye, M., & Truccolo, W. (2018). Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy. Nature Communications, 9(1), 1–15.

CAS  Google Scholar 

Saggio, M. L., Crisp, D., Scott, J. M., Karoly, P., Kuhlmann, L., Nakatani, M., Murai, T., Dmpelmann, M., Schulze-Bonhage, A., Ikeda, A., Cook, M., Gliske, S. V., Lin, J., Bernard, C., Jirsa, V. & Stacey, W. C. (2020). A taxonomy of seizure dynamotypes. Elife, 9, e55632.

Sip, V., Guye, M., Bartolomei, F., & Jirsa, V. (2021). Computational modeling of seizure spread on a cortical surface. Journal of Computational Neuroscience, 1–15.

Subasi, A., Kevric, J., & Canbaz, M. (2019). Epileptic seizure detection using hybrid machine learning methods. Neural Computing and Applications, 31(1), 317–325.

Google Scholar 

Ogata, K. (2010). Modern control engineering. Prentice Hall.

Oppenheim, A. V., Willsky, A. S., & Nawab, H. (1997). S. Prentice Hall-New Jersey: Signals and Systems.

Google Scholar 

Reyhanoglu, M., van der Schaft, A., McClamroch, N. H., & Kolmanovsky, I. (1999). Dynamics and control of a class of underactuated mechanical systems. IEEE Transactions on Automatic Control, 44(9), 1663–1671.

Google Scholar 

Rizzone, M., Lanotte, M., Bergamasco, B., Tavella, A., Torre, E., Faccani, G., et al. (2001). Deep brain stimulation of the subthalamic nucleus in Parkinson’s disease: effects of variation in stimulation parameters. Journal of Neurology, Neurosurgery & Psychiatry, 71(2), 215–219.

CAS  Google Scholar 

Sagnol, G. (2012). Picos documentation. A Python interface to conic optimization solvers.

Slotine, J. J. E. (1991). & Li. Prentice hall-Englewood Cliffs, New Jersey: W. Applied Nonlinear Control.

Google Scholar 

Spong, M. W. (1998). Underactuated mechanical systems. In Control problems in robotics and automation (pp. 135-150). Springer, Berlin, Heidelberg.

Soong, T. T. (2004). Fundamentals of probability and statistics for engineers. John Wiley & Sons.

Tanaka, K., & Wang, H. O. (2004). Fuzzy control systems design and analysis: a linear matrix inequality approach. New York: Wiley-New York.

Google Scholar 

Taniguchi, T., Tanaka, K., Ohtake, H., & Wang, H. O. (2001). Model construction, rule reduction, and robust compensation for generalized form of Takagi-Sugeno fuzzy systems. IEEE Transactions on Fuzzy Systems, 9(4), 525–538.

Google Scholar 

Tellez-Zenteno, J. F., McLachlan, R. S., Parrent, A., Kubu, C. S., & Wiebe, S. (2006). Hippocampal electrical stimulation in mesial temporal lobe epilepsy. Neurology, 66(10), 1490–1494.

CAS  PubMed  Google Scholar 

Van den Bos, A. (2007). Parameter estimation for scientists and engineers. New Jersey: John Wiley & Sons.

Velasco, F., Velasco, M., Velasco, A. L., Menez, D., & Rocha, L. (2001). Electrical stimulation for epilepsy: stimulation of hippocampal foci. Stereotactic & Functional Neurosurgery, 77(1–4), 223–227.

CAS  Google Scholar 

Velasco, A. L., Velasco, F., Velasco, M., Trejo, D., Castro, G., & Carrillo-Ruiz, J. D. (2007). Electrical stimulation of the hippocampal epileptic foci for seizure control: a double-blind, long-term follow-up study. Epilepsia, 48(10), 1895–1903.

PubMed  Google Scholar 

Vezzani, A., French, J., Bartfai, T., & Baram, T. Z. (2011). The role of inflammation in epilepsy. Nature Reviews Neurology, 7(1), 31.

CAS  PubMed  Google Scholar 

Walker, J. E., & Kozlowski, G. P. (2005). Neurofeedback treatment of epilepsy. Child and Adolescent Psychiatric Clinics, 14(1), 163–176.

Google Scholar 

Wendling, F., Benquet, P., Bartolomei, F., & Jirsa, V. (2015). Computational models of epileptiform activity. Journal of Neuroscience Methods, 260, 233–251.

PubMed  Google Scholar 

Zhang, H., & Xiao, P. (2018). Seizure dynamics of coupled oscillators with epileptor field model. International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, 28(03), 1850041.

Google Scholar 

Zhu, D., Bieger, J., Molina, G. G. & Aarts, R. M. (2010). A survey of stimulation methods used in SSVEP-based BCIs, Computational Intelligence and Neuroscience, 1–13.

留言 (0)

沒有登入
gif