Application of multidimensional scaling method for analysis of EEG features in patients with dyscirculatory encephalopathy

Kaleriya Moroz; Elena Kizhevatova; Vitaly Omelchenko; Karina Karakhanyan; Natalia Korotkieva

doi:10.1051/e3sconf/202021017005

All issues

Volume 210 (2020)

E3S Web Conf., 210 (2020) 17005

References

Open Access

Issue		E3S Web Conf. Volume 210, 2020 Innovative Technologies in Science and Education (ITSE-2020)


Article Number		17005
Number of page(s)		7
Section		Social Determinants and Health Behaviors
DOI		https://doi.org/10.1051/e3sconf/202021017005
Published online		04 December 2020

N. Sharma, M. H. Kolekar, K. Jha, Y. Kumar, EEG and Cognitive Biomarkers Based Mild Cognitive Impairment Diagnosis, IRBM, 40(2), 113-121 (2019) https://doi.org/10.1016/j.irbm.2018.11.007 [CrossRef] [Google Scholar]
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E. A. Kizhevatova, D. V. Bakuzova, V. P. Omelchenko, V. V. Efremov, Method of diagnosis of cognitive disorders of vascular origin in chronic cerebral ischemia. Pat. 2584651 Russian Federation: IPC A61B5/0476 (2006.01), no. 2015107404/14; declared. 03. 03.15;publ. 20. 05.16,Byul. 14, 3 (2015) [Google Scholar]

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[1] N. Sharma, M. H. Kolekar, K. Jha, Y. Kumar, EEG and Cognitive Biomarkers Based Mild Cognitive Impairment Diagnosis, IRBM, 40(2), 113-121 (2019) https://doi.org/10.1016/j.irbm.2018.11.007 [CrossRef] [Google Scholar]

[2] F. R. Farina, D. D. Emek-Savaş, L. Rueda-Delgado, R. Boyle, H. Kiiski, G. Yener, R. Whelan, A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment, Neuromage, 21, 116795 (2020) https://doi.org/10.1016/j.neuroimage.2020.116795 [Google Scholar]

[3] E. V. Orekhova, M. Elam, V. Yu. Orekhov, Unraveling superimposed EEG rhythms with multi-dimensional decomposition, Journal of Neuroscience Methods, 195(1), 47-60 (2011) https://doi.org/10.1016/j.jneumeth.2010.11.010 [CrossRef] [PubMed] [Google Scholar]

[4] F. Altındiş, B. Yılmaz, S. Borisenok, K. İçöz, Parameter investigation of topological data analysis for EEG signals, Biomedical Signal Processing and Control, 63, 102196 (2021) https://doi.org/10.1016/j.bspc.2020.102196 [CrossRef] [Google Scholar]

[5] K. Glomb, J. Rué Queralt, D. Pascucci, M. Defferrard, S. Tourbier, M. Carboni, M. Rubega, S. Vulliémoz, G. Plomp, P. Hagmann, Connectome spectral analysis to track EEG task dynamics on a subsecond scale, NeuroImage, 221, 117137 (2020) https://doi.org/10.1016/j.neuroimage.2020.117137 [Google Scholar]

[6] D. Jenson, D. Thornton, A. W. Harkrider, T. Saltuklaroglu, Influences of cognitive load on sensorimotor contributions to working memory: An EEG investigation of mu rhythm activity during speech discrimination, Neurobiology of Learning and Memory, 166, 107098 (2019) https://doi.org/10.1016/j.nlm.2019.107098 [Google Scholar]

[7] P. M. Keune, S. Hansen, T. Sauder, S. Jaruszowic, C. Kehm, J. Keune, E. Weber, M. Schönenberg, P. Oschmann, NeuroImage: Clinical, 22, 101716 (2019) https://doi.org/10.1016/j.nicl.2019.101716 [CrossRef] [Google Scholar]

[8] P. Marsella, A. Scorpecci, G. Cartocci, S. Giannantonio, A. G. Maglione, I. Venuti, A. Brizi, F. Babiloni, EEG activity as an objective measure of cognitive load during effortful listening: A study on pediatric subjects with bilateral, asymmetric sensorineural hearing loss, International Journal of Pediatric Otorhinolaryngology, 99, 1-7 (2017) https://doi.org/10.1016/j.ijporl.2017.05.006 [Google Scholar]

[9] V. Loganina, E. Kuimova, Methods of multi-dimensional statistical analysis for assessing the impact of applying technology of paint formulation: E3S Web Conf. 135, (2019) https://doi.org/10.1051/e3sconf/201913503039 [Google Scholar]

[10] C. Oğüt, K. Başar, Assessment of impulsivity with self-report scales and behavioral tasks in symptomatic and remitted patients with major depression: European Neuropsychopharmacology, 27, S807 (2017) https://doi.org/10.1016/S0924-977X(17)31462-1 [Google Scholar]

[11] B. Jyothia, Y. MadhaveeLathab, P. G. KrishnaMohanc, V. S. K. Reddy, Integrated Multiple Features for Tumor Image Retrieval Using Classifier and Feedback Methods. Procedia Computer Science, 85, 141-148 (2016) https://doi.org/10.1016/j.procs.2016.05.200 [CrossRef] [Google Scholar]

[12] R. Neydorf, V. Krukhmalev, N. Kudinov, V. Pshikhopov, Methods of statistical processing of meteorological data for the tasks of trajectory planning of MAAT feeders: SAE Technical Papers, 7, 100869 (2013) DOI: 10.4271/2013-01-2266. [Google Scholar]

[13] R. Neydorf, A. Neydorf, D. Vučinić, “Cut-Glue” approximation method for strongly nonlinear and multidimensional object dependencies modeling. Advanced Structured Materials, 72, 155-173 (2018) DOI: 10.1007/978-3-319-59590-0_13 [CrossRef] [Google Scholar]

[14] V. V. Bonilla, C. G. Mosquera, M. D. Mideros, A. V. Litvin, Definition of the elbow movement parameters by means of the analysis of the biceps surface electromiographic signals: Proceedings - 2017 International Conference on Information Systems and Computer Science, INCISCOS 2017, 2017, Pages 141-145 (2018) DOI: 10.1109/INCISCOS.2017.52 [Google Scholar]

[15] V. Parshina, E. Kuznetsova, Interaction between industry higher educational institutions and the customer of educational services: E3S Web Conf. 135, (2019) https://doi.org/10.1051/e3sconf/201913504001 [Google Scholar]

[16] E. A. Kizhevatova, D. V. Bakuzova, V. P. Omelchenko, V. V. Efremov, Method of diagnosis of cognitive disorders of vascular origin in chronic cerebral ischemia. Pat. 2584651 Russian Federation: IPC A61B5/0476 (2006.01), no. 2015107404/14; declared. 03. 03.15;publ. 20. 05.16,Byul. 14, 3 (2015) [Google Scholar]