IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v105y2017icp150-156.html
   My bibliography  Save this article

Modeling of epilepsy based on chaotic artificial neural network

Author

Listed:
  • Panahi, Shirin
  • Aram, Zainab
  • Jafari, Sajad
  • Ma, Jun
  • Sprott, J.C.

Abstract

Epilepsy is a long-term chronic neurological disorder that is characterized by seizures. One type of epilepsy is simple partial seizures that are localized to one area on one side of the brain, especially in the temporal lobe, but some may spread from there. GABA (gamma-aminobutyric acid) is an inhibitory neurotransmitter that is widely distributed in the neurons of the cortex. Scientists recently discovered the basic role of neurotransmitters in epilepsy. Synaptic reorganizations at GABAergic and glutamatergic synapses not only enable seizure occurrence, they also modify the normal information processing performed by these networks. Based on some physiological facts about epilepsy and chaos, a behavioral model is presented in this paper. This model represents the problem of undesired seizure, and also tries to suggest different valuable predictions about possible causes of epilepsy disorder. The proposed model suggests that there is a possible interaction between the role of excitatory and inhibitory neurotransmitters and epilepsy. The result of these studies might be helpful to discern epilepsy in a different way and give some guidance to predict the occurrence of seizures in patients.

Suggested Citation

  • Panahi, Shirin & Aram, Zainab & Jafari, Sajad & Ma, Jun & Sprott, J.C., 2017. "Modeling of epilepsy based on chaotic artificial neural network," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 150-156.
  • Handle: RePEc:eee:chsofr:v:105:y:2017:i:c:p:150-156
    DOI: 10.1016/j.chaos.2017.10.028
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077917304447
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2017.10.028?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Erkaymaz, Okan & Ozer, Mahmut & Perc, Matjaž, 2017. "Performance of small-world feedforward neural networks for the diagnosis of diabetes," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 22-28.
    2. Fister, Iztok & Perc, Matjaž & Kamal, Salahuddin M. & Fister, Iztok, 2015. "A review of chaos-based firefly algorithms: Perspectives and research challenges," Applied Mathematics and Computation, Elsevier, vol. 252(C), pages 155-165.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shabestari, Payam Sadeghi & Panahi, Shirin & Hatef, Boshra & Jafari, Sajad & Sprott, Julien C., 2018. "A new chaotic model for glucose-insulin regulatory system," Chaos, Solitons & Fractals, Elsevier, vol. 112(C), pages 44-51.
    2. Njitacke, Zeric Tabekoueng & Ramadoss, Janarthanan & Takembo, Clovis Ntahkie & Rajagopal, Karthikeyan & Awrejcewicz, Jan, 2023. "An enhanced FitzHugh–Nagumo neuron circuit, microcontroller-based hardware implementation: Light illumination and magnetic field effects on information patterns," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    3. Zhang, Ge & Wang, Chunni & Alzahrani, Faris & Wu, Fuqiang & An, Xinlei, 2018. "Investigation of dynamical behaviors of neurons driven by memristive synapse," Chaos, Solitons & Fractals, Elsevier, vol. 108(C), pages 15-24.
    4. Njitacke, Zeric Tabekoueng & Ramakrishnan, Balamurali & Rajagopal, Karthikeyan & Fonzin Fozin, Théophile & Awrejcewicz, Jan, 2022. "Extremely rich dynamics of coupled heterogeneous neurons through a Josephson junction synapse," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    5. Ding, Shoukui & Wang, Ning & Bao, Han & Chen, Bei & Wu, Huagan & Xu, Quan, 2023. "Memristor synapse-coupled piecewise-linear simplified Hopfield neural network: Dynamics analysis and circuit implementation," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    6. Toraman, Suat & Alakus, Talha Burak & Turkoglu, Ibrahim, 2020. "Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    7. Wang, Zhizhi & Hu, Bing & Zhou, Weiting & Xu, Minbo & Wang, Dingjiang, 2023. "Hopf bifurcation mechanism analysis in an improved cortex-basal ganglia network with distributed delays: An application to Parkinson’s disease," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    8. Wan, Qiuzhen & Li, Fei & Chen, Simiao & Yang, Qiao, 2023. "Symmetric multi-scroll attractors in magnetized Hopfield neural network under pulse controlled memristor and pulse current stimulation," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kostić, Srđan & Vasović, Nebojša & Sunarić, Duško, 2015. "A new approach to grid search method in slope stability analysis using Box–Behnken statistical design," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 425-437.
    2. Ivona Brajević & Jelena Ignjatović, 2019. "An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2545-2574, August.
    3. Zhou, Quan & Zhang, Wei & Cash, Scott & Olatunbosun, Oluremi & Xu, Hongming & Lu, Guoxiang, 2017. "Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization," Applied Energy, Elsevier, vol. 189(C), pages 588-601.
    4. Mostaghimi, Soudeh & Nazarimehr, Fahimeh & Jafari, Sajad & Ma, Jun, 2019. "Chemical and electrical synapse-modulated dynamical properties of coupled neurons under magnetic flow," Applied Mathematics and Computation, Elsevier, vol. 348(C), pages 42-56.
    5. Yan, Zheping & Zhang, Jinzhong & Zeng, Jia & Tang, Jialing, 2021. "Nature-inspired approach: An enhanced whale optimization algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 17-46.
    6. Sujata Dash & Ajith Abraham & Ashish Kr Luhach & Jolanta Mizera-Pietraszko & Joel JPC Rodrigues, 2020. "Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis," International Journal of Distributed Sensor Networks, , vol. 16(1), pages 15501477198, January.
    7. Izadyar, Nima & Ghadamian, Hossein & Ong, Hwai Chyuan & moghadam, Zeinab & Tong, Chong Wen & Shamshirband, Shahaboddin, 2015. "Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption," Energy, Elsevier, vol. 93(P2), pages 1558-1567.
    8. Wei, Zhouchao & Zhu, Bin & Yang, Jing & Perc, Matjaž & Slavinec, Mitja, 2019. "Bifurcation analysis of two disc dynamos with viscous friction and multiple time delays," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 265-281.
    9. Elena Niculina Dragoi & Vlad Dafinescu, 2021. "Review of Metaheuristics Inspired from the Animal Kingdom," Mathematics, MDPI, vol. 9(18), pages 1-52, September.
    10. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2019. "Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China," Renewable Energy, Elsevier, vol. 135(C), pages 984-1003.
    11. Arvinder Kaur & Saibal K. Pal & Amrit Pal Singh, 2018. "New chaotic flower pollination algorithm for unconstrained non-linear optimization functions," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(4), pages 853-865, August.
    12. Kaya, Ceren & Erkaymaz, Okan & Ayar, Orhan & Özer, Mahmut, 2018. "Impact of hybrid neural network on the early diagnosis of diabetic retinopathy disease from video-oculography signals," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 164-174.
    13. Yu, Caiyang & Cai, Zhennao & Ye, Xiaojia & Wang, Mingjing & Zhao, Xuehua & Liang, Guoxi & Chen, Huiling & Li, Chengye, 2020. "Quantum-like mutation-induced dragonfly-inspired optimization approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 178(C), pages 259-289.
    14. Shabestari, Payam Sadeghi & Panahi, Shirin & Hatef, Boshra & Jafari, Sajad & Sprott, Julien C., 2018. "A new chaotic model for glucose-insulin regulatory system," Chaos, Solitons & Fractals, Elsevier, vol. 112(C), pages 44-51.
    15. Jun, Luo & Liheng, Liu & Xianyi, Wu, 2015. "A double-subpopulation variant of the bat algorithm," Applied Mathematics and Computation, Elsevier, vol. 263(C), pages 361-377.
    16. Sabouri, Amir & Ghasemi, Mahdieh & Mehrabbeik, Mahtab, 2023. "The dynamical analysis of non-uniform neocortical network model in up-down state oscillations," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    17. Kisi, Ozgur & Shiri, Jalal & Karimi, Sepideh & Shamshirband, Shahaboddin & Motamedi, Shervin & Petković, Dalibor & Hashim, Roslan, 2015. "A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 731-743.
    18. Fister, Iztok & Perc, Matjaž & Ljubič, Karin & Kamal, Salahuddin M. & Iglesias, Andres & Fister, Iztok, 2015. "Particle swarm optimization for automatic creation of complex graphic characters," Chaos, Solitons & Fractals, Elsevier, vol. 73(C), pages 29-35.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:105:y:2017:i:c:p:150-156. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.