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Assessment of Brain Function After 240 Days Confinement Using Functional Near Infrared Spectroscopy Научная публикация

Журнал IEEE Open Journal of Engineering in Medicine and Biology
, E-ISSN: 2644-1276
Вых. Данные Год: 2024, Том: 6, Страницы: 54-60 Страниц : 7 DOI: 10.1109/OJEMB.2024.3457240
Авторы Al-Shargie F. 1 , Tariq U. 2 , Al-Ameri S. 3 , Al Hammadi A. 3 , Schastlivtseva D.V. 4 , Al-Nashash H/ 2
Организации
1 Rutgers University, New Brunswick, NJ 07102 USA
2 American University of Sharjah, Sharjah, UAE
3 Mohammed Bin Rashid Space Center (MBRSC), Dubai, UAE
4 Institute for Bio-Medical Problems, Russian Academy of Sciences, 119991 Moscow, Russia

Реферат: Future space exploration missions will expose astronauts to various stressors, making the early detection of mental stress crucial for prolonged missions. Our study proposes using functional near infrared spectroscopy (fNIRS) combined with multiple machine learning models to assess the level of mental stress. Objective: The objective is to identify and quantify stress levels during 240 days confinement scenario. In this study, we utilize a diverse set of stress indicators including salivary alpha amylase (sAA) levels, reaction time (RT) to stimuli, accuracy of target detection, and power spectral density (PSD), in conjunction with functional connectivity networks (FCN). We estimate the PSD using Fast Fourier Transform (FFT) and the FCN using partial directed coherence. Results: Our findings reveal several intriguing insights. The sAA levels increased from the first 30 days in confinement to the culmination of the lengthy 240-day mission, suggesting a cumulative impact of stress. Conversely, RT and the accuracy of target detection exhibit significant fluctuations over the course of the mission. The power spectral density shows a significant increase with time-in-mission across all participants in most of the frontal area. The FCN shows a significant decrease in most of the right frontal areas. Five different machine learning classifiers are employed to differentiate between two levels of stress resulting in impressive classification accuracy rates: 96.44% with-nearest neighbor (KNN), 95.52% with linear discriminant analysis (LDA), 88.71% with Naïve Bayes (NB), 87.41 with decision trees (DT) and 96.48% with Support Vector Machine (SVM). In conclusion, this study demonstrates the effectiveness of combining functional near infrared spectroscopy (fNIRS) with multiple machine learning models to accurately assess and quantify mental stress levels during prolonged space missions, providing a promising approach for early stress detection in astronauts.
Библиографическая ссылка: Al-Shargie F. , Tariq U. , Al-Ameri S. , Al Hammadi A. , Schastlivtseva D.V. , Al-Nashash H.
Assessment of Brain Function After 240 Days Confinement Using Functional Near Infrared Spectroscopy
IEEE Open Journal of Engineering in Medicine and Biology. 2024. V.6. P.54-60. DOI: 10.1109/OJEMB.2024.3457240 WOS Scopus OpenAlex
Даты:
Опубликована в печати: 10 сент. 2024 г.
Идентификаторы БД:
Web of science: WOS:001354664600004
Scopus: 2-s2.0-85204125250
OpenAlex: W4402389245
Цитирование в БД: Пока нет цитирований
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