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Биометрические данные и методы машинного обучения в диагностике и мониторинге нейродегенеративных заболеваний: обзор
И.А. Ходашинский 1, К.С. Сарин 1, М.Б. Бардамова 1, М.О. Светлаков 1, А.О. Слёзкин 1, Н.П. Корышев 1

1 Томский государственный университет систем управления и радиоэлектроники,
634050, Россия, г. Томск, проспект Ленина, д. 40

 PDF, 1286 kB

DOI: 10.18287/2412-6179-CO-1134

Страницы: 988-1019.

Аннотация:
Представлен обзор неинвазивных биометрических методов выявления и прогнозирования развития нейродегенеративных заболеваний. Дан анализ различных модальностей, используемых для диагностики и мониторинга. Рассмотрены такие модальности, как рукописные данные, электроэнцефалограмма, речь, походка, движение глаз, а также использование композиций данных модальностей. Проведен подробный анализ современных методов и систем принятия решений, основанных на машинном обучении. Представлены наборы данных, методы предобработки, модели машинного обучения, оценки точности при диагностике заболеваний. В заключении рассмотрены текущие открытые проблемы и будущие перспективы исследований в данном направлении.

Ключевые слова:
неинвазивные методы диагностики, нейродегенеративные заболевания, обработка биометрических сигналов, машинное обучение.

Благодарности
Работа выполнена при поддержке Российского на-учного фонда (проект № 22-21-00021).

Цитирование:
Ходашинский, И.А. Биометрические данные и методы машинного обучения в диагностике и мониторинге нейродегенеративных заболеваний: обзор / И.А. Ходашинский, К.С. Сарин, М.Б. Бардамова, М.О. Светлаков, А.О. Слёзкин, Н.П. Корышев // Компьютерная оптика. – 2022. – Т. 46, № 6. – С. 988-1019. – DOI: 10.18287/2412-6179-CO-1134.

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.

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