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Proceedings of SAI Intelligent Systems Conference

IntelliSys 2019: Intelligent Systems and Applications pp 650–670Cite as

Dynamic Scaling of EEG Fluctuations of Patients with Learning Disorders Based on Artificial Intelligence

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1038))

Abstract

This work models the dynamics of time series fluctuations of patients with learning disorders, specifically with reading-writing problems, applying fractal geometry, rough interface growth theory and Artificial Intelligence. From the EEG of children diagnosed with reading-writing problems, we obtain data of the brain activity of these children with which time series of fluctuations (standard deviations, \(\upsilon \left( t,\tau \right) \)) for each of the 19 channels distributed in different regions of the cerebral cortex. The self-affinity of the time series of fluctuations (treated as interfaces in motion) is characterized by the scaling behavior of the structure functions by one hand \(\sigma \propto \left( \delta _{t}\right) ^{\zeta }\), with \(\zeta \) as the local or roughness exponent and the other hand \(\sigma \propto \left( \tau \right) ^{\beta }\), with \(\beta \) as the fluctuation growth exponent. These findings guide us to propose the existence of a dynamic scaling behavior similar to that of Family-Vicsek for the kinetic roughening of a moving interface. In addition these findings are implemented in an Internet of Things (IoT) Network.

Oswaldo Morales Matamoros, Jesús Jaime Moreno Escobar, Teresa Ivonne Contreras Troya, Ricardo Tejeida Padilla—Research Professor (Profesor Investigador)

Ixchel Lina Reyes—Ph.D. student (Estudiante de Doctorado).

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Acknowledgment

This article is supported by National Polytechnic Institute (Instituto Poliécnico Nacional) of Mexico by means of Projects No. 20195208 and 20190046 granted by Secretariat of Graduate and Research, National Council of Science and Technology of Mexico (CONACyT). The research described in this work was carried out at the Superior School of Mechanical and Electrical Engeniering (Escuela Superior de Ingeniería Mecánica y Eléctrica) Campus Zacatenco in colboration with the UAEM University Center Campus Ecatepec (Centro Universitario UAEM Ecatepec). In addition, the authors thank to Dr. Daniel Morales Matamoros for his contributions regarding the kinetic theory of rough interfaces and to Dr. Alejandra Fávila for providing data on electroencephalograms applied to children with learning disorders. It should be noted that part of the results of this work was carried out by Doctoral students Ixchel Lina and Erika Aguilar.

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Authors and Affiliations

  1. Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Zacatenco, Instituto Politécnico Nacional, Mexico city, Mexico

    Oswaldo Morales Matamoros, Jesús Jaime Moreno Escobar & Ixchel Lina Reyes

  2. Universidad Autónoma del Estado de México, Toluca, Mexico

    Teresa Ivonne Contreras Troya

  3. Escuela Superior de Turismo, Instituto Politécnico Nacional, Mexico City, Mexico

    Ricardo Tejeida Padilla

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  1. Oswaldo Morales Matamoros
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  2. Jesús Jaime Moreno Escobar
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  3. Ixchel Lina Reyes
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  4. Teresa Ivonne Contreras Troya
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  5. Ricardo Tejeida Padilla
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Correspondence to Jesús Jaime Moreno Escobar .

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Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Matamoros, O.M., Escobar, J.J.M., Reyes, I.L., Troya, T.I.C., Padilla, R.T. (2020). Dynamic Scaling of EEG Fluctuations of Patients with Learning Disorders Based on Artificial Intelligence. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_49

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