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

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. <i>Objective:</i> 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. <i>Results:</i> 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.

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Publication Details

Journal
IEEE Open Journal of Engineering in Medicine and Biology
Published
2024-09-10
DOI
https://doi.org/10.1109/ojemb.2024.3457240
Citations
1
Primary Topic
Spectroscopy Techniques in Biomedical and Chemical Research
Type
article
Field-Weighted Citation Impact
0.66

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article

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

Fares Al-Shargie, Hasan Al‐Nashash, Saleh Al-Ameri, Schastlivtseva Daria Vladimirovna et al.
1 citations
IEEE Open Journal of Engineering in Medicine and Biology
Spectroscopy Techniques in Biomedical and Chemical Research
0.66
article

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

Fares Al-Shargie, Hasan Al‐Nashash, Saleh Al-Ameri, Schastlivtseva Daria Vladimirovna, Usman Tariq, Abdulla Al-Hammadi
article en
1 citations

Abstract

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. <i>Objective:</i> 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. <i>Results:</i> 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.

IEEE Open Journal of Engineering in Medicine and BiologyVol. 6
Rutgers, The State University of New Jersey (US), Mohammed Bin Rashid School of Government (AE), American University of Sharjah (AE), Institute of Biomedical Problems (RU)
American University of Sharjah
Openalex Percentile: Top 20%
Spectroscopy Techniques in Biomedical and Chemical Research
0.66
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