Al-Shargie Fares, Tariq Usman, Al-Ameri Saleh, Al-Hammadi Abdalla, Vladimirovna Schastlivtseva Daria, Al-Nashash Hasan
Rutgers University New Brunswick NJ 07102 USA.
American University of Sharjah Sharjah UAE.
IEEE Open J Eng Med Biol. 2024 Sep 10;6:54-60. doi: 10.1109/OJEMB.2024.3457240. eCollection 2025.
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. 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. 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.
未来的太空探索任务将使宇航员面临各种压力源，因此对于长期任务而言，早期发现精神压力至关重要。我们的研究提议使用功能近红外光谱技术（fNIRS）结合多种机器学习模型来评估精神压力水平。目的是识别并量化在240天禁闭情景下的压力水平。在本研究中，我们利用了一系列不同的压力指标，包括唾液α淀粉酶（sAA）水平、对刺激的反应时间（RT）、目标检测的准确性以及功率谱密度（PSD），并结合功能连接网络（FCN）。我们使用快速傅里叶变换（FFT）估计PSD，使用偏定向相干估计FCN。我们的研究结果揭示了几个有趣的见解。sAA水平从禁闭的前30天到漫长的240天任务结束时有所增加，这表明压力具有累积影响。相反，RT和目标检测的准确性在任务过程中呈现出显著波动。功率谱密度在大多数额叶区域随任务时间的增加而显著增加。FCN在大多数右侧额叶区域显著下降。我们使用五种不同的机器学习分类器来区分两种压力水平，从而得到了令人印象深刻的分类准确率：最近邻（KNN）为96.44%，线性判别分析（LDA）为95.52%，朴素贝叶斯（NB）为88.71%，决策树（DT）为87.41%，支持向量机（SVM）为96.48%。总之，本研究证明了将功能近红外光谱技术（fNIRS）与多种机器学习模型相结合，能够在长期太空任务中准确评估和量化精神压力水平，为宇航员早期压力检测提供了一种有前景的方法。
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