TY - GEN
T1 - Mental Stress Assessment Using fNIRS and LSTM
AU - Katmah, Rateb
AU - Al-Shargie, Fares
AU - Tariq, Usman
AU - Babiloni, Fabio
AU - Al-Mughairbi, Fadwa
AU - Al-Nashash, Hasan
N1 - Funding Information:
ACKNOWLEDGMENT The authors like to convey their appreciation to all subjects who participated in this study for their devotion during the fNIRS recording. The American University of Sharjah, United Arab Emirate, financed this research through a grant FRG2020.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Mental stress is a significant factor in the development of a wide variety of psychological, emotional, behavioral, and physical illnesses. It is critical to accurately quantify mental stress, which needs reliable neuroimaging to monitor stress levels. In this work, we used a modified Stroop Color Word Task (SCWT) with time constraints and negative feedback to elicit two distinct degrees of stress in the workplace. We then used salivary alpha amylase (SAA) concurrently with functional near-infrared spectroscopy (fNIRS) to quantify the level of stress. We propose Long Short-Term Memory (LSTM) to decode the two classes of mental stress based on the fNIRS time series. Five-fold cross validation, two LSTM layers, as well as many trainable characteristics were used to construct the network. The induced mental stress increased the level of salivary alpha amylase significantly (p<0.001) by 32.09%. Likewise, we found that LSTM classified mental stress with an average accuracy, sensitivity, and specificity, equal to 72.5%, 72%, and 73% respectively. The findings indicated that the developed LSTM could be used to effectively classify mental stress using fNIRS time series.
AB - Mental stress is a significant factor in the development of a wide variety of psychological, emotional, behavioral, and physical illnesses. It is critical to accurately quantify mental stress, which needs reliable neuroimaging to monitor stress levels. In this work, we used a modified Stroop Color Word Task (SCWT) with time constraints and negative feedback to elicit two distinct degrees of stress in the workplace. We then used salivary alpha amylase (SAA) concurrently with functional near-infrared spectroscopy (fNIRS) to quantify the level of stress. We propose Long Short-Term Memory (LSTM) to decode the two classes of mental stress based on the fNIRS time series. Five-fold cross validation, two LSTM layers, as well as many trainable characteristics were used to construct the network. The induced mental stress increased the level of salivary alpha amylase significantly (p<0.001) by 32.09%. Likewise, we found that LSTM classified mental stress with an average accuracy, sensitivity, and specificity, equal to 72.5%, 72%, and 73% respectively. The findings indicated that the developed LSTM could be used to effectively classify mental stress using fNIRS time series.
KW - LSTM.
KW - Mental Stress
KW - SCWT
KW - fNIRS
UR - http://www.scopus.com/inward/record.url?scp=85142684879&partnerID=8YFLogxK
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U2 - 10.1109/SMC53654.2022.9945309
DO - 10.1109/SMC53654.2022.9945309
M3 - Conference contribution
AN - SCOPUS:85142684879
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2287
EP - 2292
BT - 2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022
Y2 - 9 October 2022 through 12 October 2022
ER -