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DOI: https://doi.org/10.31071/kit2019.15.01


Опись-ссылка ISSN 1812-7231 Klin.inform.telemed. Volume 14, Issue 15, 2019, Pages 13-34


Автор(ы) И. В. Редька


Учреждение(я)

Харьковская медицинская академия последипломного образования МЗ Украины

Харьковский национальный университет имени В. Н. Каразина МОН Украины


Название статьи Современные подходы к обнаружению и удалению артефактов из ЭЭГ-сигналов. Обзор


Аннотация (резюме)

Введение. Обнаружение артефактов с последующим их удалением из данных ЭЭГ является важной проблемой в нейрофизиологии, поскольку характеристики артефактных фрагментов прекрываются с таковыми истинной активности головного мозга.

Цель – обзор существующих математических подходов к распознаванию и удалению артефактов с ЭЭГ-сигналов.

Результаты. Существуют методы, основанные на статистическом отклонении артефактов, адаптивной фильтрации, регрессии, разделении слепых источников, эмпирической модовой декомпозиции, вейвлет-преобразовании и их комбинации, каждый из которых имеет свои преимущества и недостатки. Тем не менее, не существует универсального алгоритма для всех типов артефактов. Первоочередными критериями в выборе алгоритма удаления артефакта является наличие опорного канала, способ выявления артефакта (автоматизированный или экспертный) и режим обнаружения/удаления артефакта (поточный или пакетный). Довольно сложно сравнивать различные методы отклонения артефактов из-за использования разных метрик, основанных на их способности удалять артефакты и степени искажения выходного сигнала.

Заключение. Анализ независимых компонентов является наиболее часто используемым в нейрофизиологических исследованиях. Приоритетным направлением является разработка гибридных методов удаления физиологических артефактов. Целесообразной является трехэтапная проверка эффективности нового алгоритма выявления/удаления артефактов.


Ключевые слова ЭЭГ, артефакты, регрессия, адаптивная фильтрация, слепое разделение источников, эмпирическая модовая декомпозиция, вейвлет-преобразование, пороговые значения


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