Developing A Seizure Prediction Algorithm for A Non-Invasive Neuromodulator

##plugins.themes.bootstrap3.article.main##

  •   Mahnaz Asgharpour

  •   Mehdi Sedighi

  •   Mohammad Reza Jahed Motlagh

Abstract

In this study, a novel real-time seizure prediction algorithm is introduced to predict epileptic seizures. The proposed algorithm is expected to be applicable in a noninvasive neuromodulator. As a model of the epileptogenic zone, a small-world network of Huber-Braun neurons was built up. To assess the effects of noninvasive stimulation techniques, such as transcranial magnetic stimulation, this network was modified, and the magneto-motive forces and the electromagnetically induced currents were further applied on the network. Comprehensive investigations of the electroencephalograms of epilepsy patients have suggested that some chaotic mechanisms generate the seizures. Hence, chaos and bifurcation theory was applied, and the induced current was considered as the bifurcation parameter. The bifurcation diagram of the 'inter-spike' intervals of the mean voltage of the small world network was obtained. The precise time at which the bifurcation took place was subsequently considered as the time of the seizure onset. Comparisons of the bifurcation diagrams obtained from the patients’ electroencephalographs showed that the proposed network model could reasonably represent the actual neuronal networks of the epileptogenic zone. A dataset of the electroencephalographs of epilepsy patients and normal volunteers from an epilepsy center in Germany was used to validate the prediction algorithm. The simulation results show that the proposed algorithm has a significant capability to predict the precise occurrence of seizures and the achieved sensitivity, accuracy, and specificity of this approach were remarkably higher than those reported in previous studies.


Keywords: Bifurcation Theory, Epileptic Seizure Prediction, Epileptic Seizure Suppression, Magnetic Stimulation, Responsive Neuro-Stimulation

References

C. Y. Lin et al., “Implantable stimulator for epileptic seizure suppression with loading impedance adaptability”, IEEE Trans. Biomed. Circuits Syst., vol. 7, no. 2, pp. 196–203, 2013.

K. Lehnertz et al., “State-of-the-art of seizure prediction”, J. Clin. Neurophysiol. , vol. 24, pp.147–53, 2007.

C.L. Chen et al., “Application of Chaos Theory and Data Mining to Seizure Detection of Epilepsy”, proceedings of 2012 IACSIT Hong Kong Conferences, IPCSIT, vol. 25, pp.23-8, 2012.

M.J. Cook et al., “Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study”, Lancet Neurol., vol. 12, pp. 563–71, 2013.

R. Tetzlaff et al., "Automated detection of a pre-seizure state: nonlinear EEG analysis in epilepsy by cellular nonlinear networks and Volterra systems", International Journal of Circuit Theory and Applications, vol.34, pp. 89–108, 2006.

S.G. Dastidar, “Models of EEG Data Mining and Classification in Temporal Lobe Epilepsy: Wavelet-Chaos-Neural Network Methodology and Spiking Neural Networks”, Ph.D. Dissertation, Ohio State University, 2007.

M. Asgharpour and B. Moaveni, “Bifurcation Control in Hodgkin–Huxley Model based on the Digital State Feedback Theory”, Proceedings of 2010 International Conference on Modeling, Simulation and Control (ICMSC), pp. 211-16, 2010.

[8] M. Asgharpour et al., “A Study on a New Bifurcation Parameter in a Modified Huber-Braun Model”, Technical Gazette, Vol. 24, no. 2, pp. 379-384, 2017.

M. Salam et al., “A novel low-power-implantable epileptic seizure-onset detector”, IEEE Trans. Biomed. Circuits Syst., vol. 5, no. 6, pp. 568–78, Dec. 2011.

S.C. Schachter et al., “Advances in the Application of Technology to Epilepsy: The CIMIT/NIO Epilepsy Innovation Summit”, Epilepsy & Behavior, vol. 16, pp.3–46, 2009.

D.L. Shen and Y.J. Chu, “A Linearized Current Stimulator for Deep Brain Stimulation”, Proceedings of 32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina, pp. 6485-9, September 2010.

P. Nadeau and M. Sawan, “A flexible high voltage biphasic current-controlled stimulator”, Proceedings of the Conference on Biomedical Circuits and Systems, pp.206–9, 2006.

L.D. Iasemidis and J.C. Sackellares, “Chaos Theory and Epilepsy”, The Neuroscientist, available online at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.40.7741&rep=rep1&type=pdf, 1996.

M. Kobayashi, A.P. Leone, “Transcranial magnetic stimulation in neurology”, Lancet Neurology, Vol. 2, pp. 145–156, 2003.

R. Chen R et al., “Depression of motor cortex excitability by low-frequency transcranial magnetic stimulation”, Neurology, Vol. 48, pp.1398-1403, 1997.

M. Beuler et al., “FPGA Implementation of the Huber-Braun Neuron Model”, Proceedings of the 9th International Joint Conference on Computational Intelligence (IJCCI 2017), pp.247-254, 2017.

U. Feudel et al., “Homoclinic bifurcation in a Hodgkin–Huxley model of thermally sensitive neurons”, Chaos, vol. 10, pp. 231-40, March 2000.

C. A. S. Batista et al., “Control of bursting synchronization in networks of Hodgkin-Huxley-type neurons with chemical synapses” Physical Review E 87, 042713, pp.1-13, 2013.

L. Tian, D. Li, and X. Sun, "Nonlinear-estimator-based robust synchronization of Hodgkin–Huxley neurons", Neurocomputing, vol. 72, pp. 186-96, 2008.

Available at http://epileptologie-bonn.de/cms/front_content.php?idcat=193〈=3&changelag=3.

C.Y. Huang et al., (2005). 'Influence of Local Information on Social Simulations in Small-World Network Models'. Journal of Artificial Societies and Social Simulation, Vol. 8, no.4, http://jasss.soc.surrey.ac.uk/8/4/8.html, 2005.

M. Amiri et al., "Bifurcation Analysis of the Poincare map function of intracranial EEG signals in temporal lobe epilepsy patients", Mathematics and Computers in Simulation, Vol. 81, pp. 2471–2491, 2011.

S. Raeisdana et al., "An evolutionary-network model of epileptic phenomena", Neurocomputing, Vol. 74, pp. 617-628, 2011.

N. Chakravarthy et al., “Homeostasis of Brain Dynamics in Epilepsy: A Feedback Control Systems Perspective of Seizures”, Ann. Biomed. Eng., vol. 37, no.3, pp.565–85, March 2009.

C.C. Yang and C.L. Lin, “Robust adaptive sliding mode control for synchronization of space-clamped FitzHugh–Nagumo neurons”, Nonlinear Dyn., vol. 69, pp. 2089–96, 2012.

R. J. Ilmoniemi et al., “Methodology for Combined TMS and EEG”, Brain Topography, Vol. 22, no. 4, pp. 233–248, 2010.

P. C. Taylor et al., “Combining TMS and EEG to study cognitive function and cortico-cortico interactions”, Behavioral Brain Research, Vol. 191, no. 2, pp. 141-147, 2008.

M.T. Rosenstein et al., “A practical method for calculating largest Lyapunov exponents from small data sets", Boston University Press, 1992.

M. C. Smith, “Neuronal Modeling of Focality Enhancements using Steerable Subwavelength Magnetic Arrays for Transcranial Magnetic Stimulation”, Applied Electromagnetics Research Group - Electrical Engineering, San Diego University, pp.1-13, 2016.

A. Baratloo et al., "Part 1: Simple Definition and Calculation of Accuracy, Sensitivity, and Specificity", Emergency, Vol. 3, no. 2, pp. 48-49, 2015.

R. Yadav et al., “ A novel dual-stage classifier for automatic detection of epileptic seizures ”, Proceedings of Engineering in Medicine and Biology Society, 30th Annual International Conference of the IEEE, pp. 911–14, 2008.

G. Sukhi and G. Jean, “An automatic warning system for epileptic seizures recorded on intracerebral EEGs”, Clinical Neurophysiology, vol. 116, pp. 2460–72, 2005.

A.B. Gardner et al., “One-class novelty detection for seizure analysis from intracranial EEG”, JMLR, vol.7, pp.1025–44, 2006.

L.D. Iasemidis et al., “Adaptive epileptic seizure prediction system”, IEEE Transactions on Biomedical Engineering, vol. 50, pp. 616–27, 2003.

K.A. Davis et al., “A novel implanted device to wirelessly record and analyze continuous intracranial canine EEG”, Epilepsy Res., vol. 96, pp.116–22, 2011.

K.H. Hsu et al., “Analysis of Efficiency of Magnetic stimulation”, IEEE Trans. Biomed.Eng. , Vol.50, No. 11, PP, 1276-1285, Nov 2003.

B.J. Roth and P.J. Basser, “A Model of the Stimulation of a Nerve Fiber by Electromagnetic Induction”, IEEE Trans. Biomed. Eng., vol. 37, No. 6, PP. 588-597.

T. Herbsman et al., “Motor Threshold in Transcranial Magnetic Stimulation: The Impact of White Matter Fiber Orientation and Skull-to-Cortex Distance”, Vol. 30, no. 7, pp. 2044-2055, 2010.

Downloads

Download data is not yet available.

##plugins.themes.bootstrap3.article.details##

How to Cite
[1]
Asgharpour, M., Sedighi, M. and Jahed Motlagh, M.R. 2020. Developing A Seizure Prediction Algorithm for A Non-Invasive Neuromodulator. European Journal of Engineering Research and Science. 5, 6 (Jun. 2020), 715-724. DOI:https://doi.org/10.24018/ejers.2020.5.6.1920.