Abstract
Dementia being a syndrome caused by a brain disease of a chronic or progressive nature, in which the irreversible loss of intellectual abilities, learning, expressions arises; including memory, thinking, orientation, understanding and adequate communication, of organizing daily life and of leading a family, work and autonomous social life; leads to a state of total dependence; therefore, its early detection and classification is of vital importance in order to serve as clinical support for physicians in the personalization of treatment programs. The use of the electroencephalogram as a tool for obtaining information on the detection of changes in brain activities. This article reviews the types of cognitive spectrum dementia, biomarkers for the detection of dementia, analysis of mental states based on electromagnetic oscillations, signal processing given by the electroencephalogram, review of processing techniques, results obtained where it is proposed the mathematical model about neural networks, discussion and finally the conclusions.
Keywords
This work was supported by the Universidad de las Fuerzas Armadas, Sangolquí – Ecuador. Luis A. Guerra work at the University of the Fuerzas Armadas at the campus in Latacunga City. Laura C. Lanzarini work at the National University of the Plata – Argentina and Luis E. Sanchez work at the University of the Mancha - Spain.
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Guerra, L.A., Lanzarini, L.C., Sánchez, L.E. (2018). The Electroencephalogram as a Biomarker Based on Signal Processing Using Nonlinear Techniques to Detect Dementia. In: Rocha, Á., Guarda, T. (eds) Developments and Advances in Defense and Security. MICRADS 2018. Smart Innovation, Systems and Technologies, vol 94. Springer, Cham. https://doi.org/10.1007/978-3-319-78605-6_11
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