Geodesic
On possibility of machine learning application for diagnosing dementia by EEG signals
Matematičeskaâ biologiâ i bioinformatika, Tome 14 (2019) no. 2, pp. 543-553 
I. V. Dorovskih; O. V. Sen'ko; V. Ya. Chuchupal; A. A. Dokukin; A. V. Kuznetsova. On possibility of machine learning application for diagnosing dementia by EEG signals. Matematičeskaâ biologiâ i bioinformatika, Tome 14 (2019) no. 2, pp. 543-553. http://geodesic.mathdoc.fr/item/MBB_2019_14_2_a4/
@article{MBB_2019_14_2_a4,
     author = {I. V. Dorovskih and O. V. Sen'ko and V. Ya. Chuchupal and A. A. Dokukin and A. V. Kuznetsova},
     title = {On possibility of machine learning application for diagnosing dementia by {EEG} signals},
     journal = {Matemati\v{c}eska\^a biologi\^a i bioinformatika},
     pages = {543--553},
     year = {2019},
     volume = {14},
     number = {2},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MBB_2019_14_2_a4/}
}
TY  - JOUR
AU  - I. V. Dorovskih
AU  - O. V. Sen'ko
AU  - V. Ya. Chuchupal
AU  - A. A. Dokukin
AU  - A. V. Kuznetsova
TI  - On possibility of machine learning application for diagnosing dementia by EEG signals
JO  - Matematičeskaâ biologiâ i bioinformatika
PY  - 2019
SP  - 543
EP  - 553
VL  - 14
IS  - 2
UR  - http://geodesic.mathdoc.fr/item/MBB_2019_14_2_a4/
LA  - ru
ID  - MBB_2019_14_2_a4
ER  - 
%0 Journal Article
%A I. V. Dorovskih
%A O. V. Sen'ko
%A V. Ya. Chuchupal
%A A. A. Dokukin
%A A. V. Kuznetsova
%T On possibility of machine learning application for diagnosing dementia by EEG signals
%J Matematičeskaâ biologiâ i bioinformatika
%D 2019
%P 543-553
%V 14
%N 2
%U http://geodesic.mathdoc.fr/item/MBB_2019_14_2_a4/
%G ru
%F MBB_2019_14_2_a4

Voir la notice de l'article provenant de la source Math-Net.Ru

The purpose of this study was to investigate the possibility to use electroencephalography for early diagnostics of dementia and for objective assessment of disease severity and neurometabolic treatment results. The study was based on application of machine learning methods for computer diagnosis of dementia by the energy spectra of EEG signals. Effectiveness of various machine learning technologies was investigated in order to separate different groups of patients with varying severity of dementia from healthy ones and patients with pre-dementia disorders according to the vectors of spectral indicators. Applying of cross-validation procedure showed that separation of the group with dementia from the group of people with normal physiological aging and groups of young people reaches 0.783 and 0.786, respectively by parameter ROC AUC. The results of the study allow to make an assumption, that the algorithmic assessment of dementia severity by EEG corresponds to the actual course of the disease. So, the number of cases with algorithmically identified positive dynamics significantly exceeds the number of cases with algorithmically detected negative dynamics after neurometabolic therapy in the group with mild dementia. In a combined group with both average and heavy severity of the disease such an increase was not observed.

[1] S. I. Gavrilova, Psikhicheskie rasstroistva v naselenii pozhilogo i starcheskogo vozrasta (kliniko-statisticheskoe i kliniko-epidemiologicheskoe issledovanie), diss. dokt. med. nauk, M., 1984

[2] S. I. Gavrilova, Ya. B. Kalyn, “Sotsialno-sredovye faktory i sostoyanie psikhicheskogo zdorovya pozhilogo naseleniya”, Vestnik Ros. AMN, 2002, no. 9, 15–20

[3] S. I. Gavrilova, Farmakoterapiya bolezni Altsgeimera, Puls, M., 2007, 306 pp.

[4] O. V. Bozhko, N. M. Mikhailova, N. Yu. Savvateeva, “Kliniko-tomograficheskie korrelyatsii smeshannykh dementsii altsgeimerovsko-sosudistogo geneza”, Pisikhiatriya, 46:4 (2010), 12–20

[5] E. Kokmen, V. Chandra, B. S. Schoenberg, “Trends in incidence of dementing illness in Rochester, Minnesota, in three quinquennial periods, 1960-1974”, Neurology, 38 (1988), 975–980 | DOI

[6] A. Lowin, M. Knapp, P. McCrone, “Alzheimer's disease in the UK: comparative evidence on cost of illness and volume of health services research funding”, Int. J. Geriatr. Psychiatry, 16 (2001), 1143–1148 | DOI

[7] J. Leon, P. J. Neumann, “The cost of Alzgeimer's disease in managed care: a cross-sectional study”, Am. J. Manag. Care, 5 (1999), 867–877

[8] N. Houmani, F. Vialette, E. Gallego-Jutgla, G. Dreyfus, V. H. Nguen-Michel, J. Mariani, K. Kinugawa, “Diagnosis of Alzheimer's disease with Electroencephalography in differential framework”, PLoS ONE, 13:3 (2018), e0193607 | DOI

[9] G. Fiscon, E. Weitschek, A. Cialini, G. Felici, P. Bertolazzi, S. De Salvo, P. Bramanti, M. C. De Cola, “Combining EEG signal processing with supervised methods for Alzheimer's patients classification”, BMC Med. Inform. Decis. Mak., 18:1 (2018), 35 | DOI

[10] S. Simpraga, R. Alvarez-Jimenez, H. D. Mansvelder, J. M. A. van Gerven, G. J. Groenveld, S. S. Poil, K. LinkenKaer-Hansen, “EEG Machine learning for accurate detection of cholinergic intervention and Alzheimer's disease”, SciRep, 7:1 (2017), 5775

[11] J. S. Cramer, “The early origins of the logit model”, Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 35:4 (2004), 613–626 | DOI

[12] F. Rosenblatt, “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain”, Cornell Aeronautical Laboratory, Psychological Review, 65:6 (1958), 386–408 | MR

[13] C. Cortes, V. Vapnik, “Support-vector networks”, Mach. Learn., 20:3 (1995), 273–297 | Zbl

[14] L. Breiman, “Random Forests”, Machine Learning, 45:1 (2001), 5–32 | DOI | MR | Zbl

[15] Y. Freund, R. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting”, Journal of Computer and System Sciences, 55 (1997), 119–139 | DOI | MR | Zbl

[16] J. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine”, The Annals of Statistics, 29:5 (2001), 1189–1232 | DOI | MR | Zbl

[17] V. V. Ryazanov, “Logicheskie zakonomernosti v zadachakh raspoznavaniya (parametricheskii podkhod)”, Zh. vychisl. matem. i matem. fiz., 47:10 (2007), 1793–1808 | MR

[18] V. A. Kuznetsov, O. V. Senko, A. V. Kuznetsova, L. P. Semenova, A. V. Aleschenko, T. B. Gladysheva, A. V. Ivshina, “Raspoznavanie nechetkikh sistem po metodu statisticheski vzveshennykh sindromov i ego primenenie dlya immunogematologicheskoi kharakteristiki normy i khronicheskoi patologii”, Khim. fizika, 15:1 (1996), 81–100

[19] K. Spackman, “Signal detection theory: Valuable tools for evaluating inductive learning”, Proceedings of the Sixth International Workshop on Machine Learning, Morgan Kaufmann, San Mateo, CA, 1989, 160–163 | DOI | MR

[20] H. Mann, D. Whitney, “On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other”, Annals of Mathematical Statistics, 18:1 (1947), 50–60 | DOI | MR | Zbl