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Biometric data and machine learning methods in the diagnosis and monitoring of neurodegenerative diseases: a review
I.A. Hodashinsky 1, K.S. Sarin 1, M.B. Bardamova 1, M.O. Svetlakov 1, A.O. Slezkin 1, N.P. Koryshev 1

1 Tomsk State University of Control Systems and Radioelectronics,
634050, Russia, Tomsk, Lenina avenue, 40

 PDF, 1286 kB

DOI: 10.18287/2412-6179-CO-1134

Pages: 988-1019.

Full text of article: Russian language.

Abstract:
A review of noninvasive biometric methods for detecting and predicting neurodegenerative diseases is presented. An analysis of various modalities used to diagnose and monitor diseases is given. Such modalities as handwritten data, electroencephalography, speech, gait, eye movement, as well as the use of compositions of these modalities are considered. A detailed analysis of modern methods and solutions based on machine learning is conducted. Data sets, preprocessing methods, machine learning models, and accuracy estimates for disease diagnosis are presented. In the conclusion current open problems and future prospects of research in this direction are considered.

Keywords:
non-invasive diagnostic methods, neurodegenerative diseases, biometric signal processing, machine learning.

Citation:
Hodashinsky IA, Sarin KS, Bardamova MB, Svetlakov MO, Slezkin AO, Koryshev NP. Biometric data and machine learning methods in the diagnosis and monitoring of neurodegenerative diseases: a review. Computer Optics 2022; 46(6): 988-1019. DOI: 10.18287/2412-6179-CO-1134.

Acknowledgements:
This work was supported by the Russian Science Foundation (project no. 22-21-00021).

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