TY - GEN
T1 - Dynamic scaling of EEG fluctuations of patients with learning disorders based on artificial intelligence
AU - Matamoros, Oswaldo Morales
AU - Escobar, Jesús Jaime Moreno
AU - Reyes, Ixchel Lina
AU - Troya, Teresa Ivonne Contreras
AU - Padilla, Ricardo Tejeida
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - 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, υ (t,τ)) 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 σ ∝ (δt)ζ, with ζ as the local or roughness exponent and the other hand σ ∝ (τ)β, with β 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.
AB - 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, υ (t,τ)) 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 σ ∝ (δt)ζ, with ζ as the local or roughness exponent and the other hand σ ∝ (τ)β, with β 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.
KW - Artificial Intelligence
KW - Dynamic scaling
KW - EEG
KW - Fluctuations
KW - IoT Network
KW - Roughness
KW - Self-affinity
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=85072818770&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-29513-4_49
DO - 10.1007/978-3-030-29513-4_49
M3 - Contribución a la conferencia
AN - SCOPUS:85072818770
SN - 9783030295127
T3 - Advances in Intelligent Systems and Computing
SP - 650
EP - 670
BT - Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference IntelliSys Volume 2
A2 - Bi, Yaxin
A2 - Bhatia, Rahul
A2 - Kapoor, Supriya
PB - Springer Verlag
T2 - Intelligent Systems Conference, IntelliSys 2019
Y2 - 5 September 2019 through 6 September 2019
ER -