Abstract
Stress is one of the major contributing factors which lead to various diseases including cardiovascular diseases. To avoid this, stress monitoring is very essential for clinical intervention and disease prevention. In present study, the feasibility of exploiting Electroencephalography (EEG) signals to monitor stress in mental arithmetic tasks is investigated. This paper presents a novel hardware system along with software system which provides a method for determining stress level with the help of a Theta sub-band of EEG signals. The proposed system performs a signal-processing of EEG signals, which recognizes the peaks of the Theta sub-band above a certain threshold value. It finds the first order difference information to identify the peak. This proposed method of EEG based stress detection can be used as quick, noninvasive, portable and handheld tool for determining the stress level of a person.
Keywords
Research Grant support from: BIRAC GYTI, India.
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Mcewen, B.S.: Central effects of stress hormones in health and disease: understanding the protective and damaging effects of stress and stress mediators. Eur. J. Pharmacol. 583(2–3), 174–185 (2008)
Cohen, S., Janicki-Deverts, D., Miller, G.E.: Psychological stress and disease. J. Am. Med. Assoc. 298(14), 1685–1687 (2007)
Steptoe, A., Kivimaki, M.: Stress and cardiovascular disease. Nat. Rev. Cardiol. 9(1), 360–370 (2012)
Berka, C., Levendowski, D.J., Cvetinovic, M.M., Petrovic, M.M., Davis, G., Lumicao, M.N., Zivkovic, V.T., Popovic, M.V., Olmstead, R.: Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset. Int. J. Hum. Comput. Interact. 17(2), 151–170 (2004)
Kulkarni, N.N., Bairagi, V.K.: Extracting salient features for EEG-based diagnosis of Alzheimer’s disease using support vector machine classifier. IETE J. Res. 63(1), 11–22 (2016)
Kulkarni, N.N., Bairagi, V.K.: Electroencephalogram based diagnosis of Alzheimer Disease. In: 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), pp 1–6 (2015)
Wang, X., Nie, D., Lu, B.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94–106 (2014)
Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion recognition from brain signals using hybrid adaptive filtering and higher order crossings analysis. IEEE Trans. Affect. Comput. 1(2), 81–97 (2010)
Tatum, W.O.: Handbook of EEG Interpretation, pp. 28–34. Demos Medical Publishing, New York (2014)
Gayakwad, R.A.: Op-amps and Linear Integrated Circuits, pp. 249–298. Prentice-Hall, Englewood Cliffs (1988)
Taelman, J., Vandeput, S., Spaepen, A., Huffel, S.: Influence of mental stress on heart rate and heart rate variability. In: 4th Springer European Conference of the International Federation for Medical and Biological Engineering, IFMBE Proceedings, vol. 22, pp. 1366–1369 (2008)
Patil, K., Singh, M., Singh, G., Anjali, S.N.: Mental stress evaluation using heart rate variability analysis: a review. Int. J. Public Ment. Health Neurosci. 2(1), 10–16 (2015)
Abouelenien, M., Burzo, M., Mihalcea, R.: Human acute stress detection via integration of physiological signals and thermal imaging. In: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, p. 32. ACM (2016)
Vanitha, L., Suresh, G.R.: Hierarchical SVM to detect mental stress in human beings using heart rate variability. In: 2nd International Conference on Devices, Circuits and Systems (ICDCS), pp. 1–5 (2014)
Sun, F., Kuo, C., Cheng, H., Buthpitiya, S., Collins, P., Griss, M.: Activity-aware mental stress detection using physiological sensors. In: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Mobile Computing, Applications, and Services, pp. 211–230 (2012)
Siegler, J.C., Rehman, S., Bhumireddy, G.P., Abdula, R., Klem, I., Brener, S.J., Heitner, J.F.: The accuracy of the electrocardiogram during exercise stress test based on heart size. PLoS ONE 6(8), e23044 (2011)
Hou, X., Liu, Y., Sourina, O., Mueller-Wittig, W.: CogniMeter: EEG-based emotion, mental workload and stress visual monitoring. In: International Conference on Cyberworlds (CW), pp 153–160 (2015)
Jun, G., Smitha, K.G.: EEG based stress level identification. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 3270–3274 (2016)
Vanitha, V., Krishnan, P.: Real time stress detection system based on EEG signals. Biomed. Res. 1(1), 1–5 (2016)
Al-Shargie, F., Kiguchi, M., Badruddin, N., Dass, S.C., Hani, A.F.M., Tang, T.B.: Mental stress assessment using simultaneous measurement of EEG and fNIRS. Biomed. Opt. Express 7(10), 3882–3898 (2016)
Salai, M., Vassanyi, I., Kosa, I.: Stress detection using low cost heart rate sensors. J. Healthc. Eng. 2016, 1–13 (2016)
Toth, V.: Measurement of stress intensity using EEG. Unpublished master’s thesis. Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics 2015
Rodriguez, M.: Mental stress detection using multimodal sensing in a wireless body area network. In: Informatiktage, pp. 163–166 (2012)
Acknowledgment
The authors would like to thank the BIRAC GYTI for financial supporting this work under research grant for researchers and the SKN General Hospital, Pune for their valuable help and support. The author would like to thank all authors of the references which have been used, as well as reviewers of the paper. The authors would like to thank the SERB-ITS for its travel grant support.
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Bairagi, V., Kulkarni, S. (2019). A Novel Method for Stress Measuring Using EEG Signals. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-030-03405-4_47
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DOI: https://doi.org/10.1007/978-3-030-03405-4_47
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