Nothing Special   »   [go: up one dir, main page]

Skip to main content

A Multi-Modal Dataset (MMSD) for Acute Stress Bio-Markers

  • Conference paper
  • First Online:
Biomedical Engineering Systems and Technologies (BIOSTEC 2022)

Abstract

The experiment presented in this study aimed at eliciting two affective states at pre-determined periods including relaxation and stress using various stressors. We advance the hypothesis that it is possible to observe patterns and variations in physiological signals caused by mental stress. In this chapter, an exhaustive description of the experimental protocol for signal acquisition is provided to ensure both reproducibility and repeatability. Details are presented on the whole process from the choice of sensors and stressors to the experimental design and the collected data so that the potential user of our database can have a global view and a deep understanding of the data.

Four physiological signals are recorded throughout the experiment in order to study their correlation with mental stress: electrocardiogram (ECG), photoplethysmogram (PPG), electrodermal activity (EDA) and electromyogram (EMG).

A statistical analysis is carried out for preliminary results and for protocol validation before a deeper analysis using artificial intelligence algorithms in future work.

Supported by INSEAD-Sorbonne University Behavioural Lab.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Barandas, M., et al.: TSFEL: time series feature extraction library. SoftwareX 11, 100456 (2020)

    Article  Google Scholar 

  2. Bartels, R., Peçanha, T.: HRV: a pythonic package for heart rate variability analysis. Github (2020). https://github.com/rhenanbartels/hrv/tree/0.2.8, https://doi.org/10.5281/zenodo.3960216. Accessed Oct 2021

  3. Benchekroun, M., Chevallier, B., Istrate, D., Zalc, V., Lenne, D.: Preprocessing methods for ambulatory HRV analysis based on HRV distribution, variability and characteristics (DVC). Sensors 22(5), 1984 (2022)

    Article  Google Scholar 

  4. Benchekroun, M., Chevallier, B., Zalc, V., Istrate, D., Lenne, D., Vera, N.: Analysis of the impact of inter-beat-interval interpolation on real-time HRV feature estimation for e-health applications. In: JETSAN 2021-Colloque en Télésanté et dispositifs biomédicaux-8ème édition (2021)

    Google Scholar 

  5. Benchekroun, M., Istrate, D., Zalc, V., Lenne, D.: Mmsd: a multi-modal dataset for real-time, continuous stress detection from physiological signals. In: HEALTHINF, pp. 240–248 (2022)

    Google Scholar 

  6. Boucsein, W.: Principles of electrodermal phenomena. In: Electrodermal activity, pp. 121–122. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-1126-0_1

  7. Colizzi, M., Costa, R., Todarello, O.: Transsexual patients’ psychiatric comorbidity and positive effect of cross-sex hormonal treatment on mental health: results from a longitudinal study. Psychoneuroendocrinology 39, 65–73 (2014)

    Article  Google Scholar 

  8. Dawson, M.E., Schell, A.M., Filion, D.L.: The electrodermal system (2017)

    Google Scholar 

  9. Dickerson, S.S., Gruenewald, T.L., Kemeny, M.E.: Psychobiological responses to social self threat: functional or detrimental? Self identity 8(2–3), 270–285 (2009)

    Article  Google Scholar 

  10. Dickerson, S.S., Kemeny, M.E.: Acute stressors and cortisol responses: a theoretical integration and synthesis of laboratory research. Psychol. Bull. 130(3), 355 (2004)

    Article  Google Scholar 

  11. Elgendi, M., Jonkman, M., DeBoer, F.: Heart rate variability and the acceleration plethysmogram signals measured at rest. In: Fred, A., Filipe, J., Gamboa, H. (eds.) BIOSTEC 2010. CCIS, vol. 127, pp. 266–277. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-18472-7_21

    Chapter  Google Scholar 

  12. Fink, G.: Chapter 1-stress, definitions, mechanisms, and effects outlined: lessons from anxiety. In: Fink, G. (ed.) Stress: Concepts, Cognition, Emotion, and Behavior, pp. 3–11 (2016)

    Google Scholar 

  13. Foley, P., Kirschbaum, C.: Human hypothalamus-pituitary-adrenal axis responses to acute psychosocial stress in laboratory settings. Neurosci. Biobehav. Rev. 35(1), 91–96 (2010)

    Article  Google Scholar 

  14. Giakoumis, D., et al.: Using activity-related behavioural features towards more effective automatic stress detection (2012)

    Google Scholar 

  15. Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., Tsiknakis, M.: Review on psychological stress detection using biosignals. IEEE Trans. Affect. Comput. 13(1), 440–460 (2019)

    Article  Google Scholar 

  16. Greco, A., Valenza, G., Lanata, A., Scilingo, E.P., Citi, L.: cvxEDA: a convex optimization approach to electrodermal activity processing. IEEE Trans. Biomed. Eng. 63(4), 797–804 (2015)

    Google Scholar 

  17. Grossman, P.: Respiration, stress, and cardiovascular function. Psychophysiology 20(3), 284–300 (1983)

    Article  Google Scholar 

  18. Healey, J.A., Picard, R.W.: Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 6(2), 156–166 (2005)

    Article  Google Scholar 

  19. Hoffmann, E.: Brain training against stress: theory, methods and results from an outcome study. Stress Rep. 4(2), 1–24 (2005)

    Google Scholar 

  20. Karthikeyan, P., Murugappan, M., Yaacob, S.: Descriptive analysis of skin temperature variability of sympathetic nervous system activity in stress. J. Phys. Ther. Sci. 24(12), 1341–1344 (2012)

    Article  Google Scholar 

  21. Koelstra, S., et al.: DEAP: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2011)

    Article  Google Scholar 

  22. Koldijk, S., Sappelli, M., Verberne, S., Neerincx, M.A., Kraaij, W.: The swell knowledge work dataset for stress and user modeling research. In: Proceedings of the 16th International Conference on Multimodal Interaction, pp. 291–298 (2014)

    Google Scholar 

  23. Makowski, D., et al.: NeuroKit2: a python toolbox for neurophysiological signal processing. Behav. Res. Methods 53(4), 1689–1696 (2021)

    Article  Google Scholar 

  24. Matousek, R.H., Dobkin, P.L., Pruessner, J.: Cortisol as a marker for improvement in mindfulness-based stress reduction. Complement. Ther. Clin. Pract. 16(1), 13–19 (2010)

    Article  Google Scholar 

  25. McDuff, D., Gontarek, S., Picard, R.: Remote measurement of cognitive stress via heart rate variability. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2957–2960. IEEE (2014)

    Google Scholar 

  26. Michels, N., et al.: Children’s heart rate variability as stress indicator: association with reported stress and cortisol. Biol. Psychol. 94(2), 433–440 (2013)

    Article  Google Scholar 

  27. Nikula, R.: Psychological correlates of nonspecific skin conductance responses. Psychophysiology 28(1), 86–90 (1991)

    Article  Google Scholar 

  28. Onorati, F., Barbieri, R., Mauri, M., Russo, V., Mainardi, L.: Reconstruction and analysis of the pupil dilation signal: application to a psychophysiological affective protocol. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5–8. IEEE (2013)

    Google Scholar 

  29. Pakarinen, T., Pietilä, J., Nieminen, H.: Prediction of self-perceived stress and arousal based on electrodermal activity. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2191–2195. IEEE (2019)

    Google Scholar 

  30. Picard, R.W., Vyzas, E., Healey, J.: Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1175–1191 (2001)

    Article  Google Scholar 

  31. Qi, M., Gao, H., Guan, L., Liu, G., Yang, J.: Subjective stress, salivary cortisol, and electrophysiological responses to psychological stress. Front. Psychol. 7, 229 (2016)

    Article  Google Scholar 

  32. Scheirer, J., Fernandez, R., Klein, J., Picard, R.W.: Frustrating the user on purpose: a step toward building an affective computer. Interact. Comput. 14(2), 93–118 (2002)

    Article  Google Scholar 

  33. Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., Van Laerhoven, K.: Introducing wesad, a multimodal dataset for wearable stress and affect detection. In: Proceedings of the 20th ACM International Conference on Multimodal Interaction, pp. 400–408 (2018)

    Google Scholar 

  34. Sedghamiz, H.: Complete Pan-Tompkins implementation ECG QRS detector. MATLAB Cent.: Commun. Profile 172 (2014). http://www.mathworks.com/matlabcentral/profile/authors/2510422-hooman-sedghamiz

  35. Selye, H.: The evolution of the stress concept: the originator of the concept traces its development from the discovery in 1936 of the alarm reaction to modern therapeutic applications of syntoxic and catatoxic hormones. Am. Sci. 61(6), 692–699 (1973)

    Google Scholar 

  36. Shaffer, F., Ginsberg, J.P.: An overview of heart rate variability metrics and norms. Front. Public Health 5, 258 (2017)

    Article  Google Scholar 

  37. Warttig, S.L., Forshaw, M.J., South, J., White, A.K.: New, normative, English-sample data for the short form perceived stress scale (PSS-4). J. Health Psychol. 18(12), 1617–1628 (2013)

    Article  Google Scholar 

  38. Yaribeygi, H., Panahi, Y., Sahraei, H., Johnston, T.P., Sahebkar, A.: The impact of stress on body function: a review. EXCLI J. 16, 1057 (2017)

    Google Scholar 

Download references

Acknowledgements

Authors would like to thank Idex Sorbonne University for funding this experimental study as part of french state support for ”Investissements d’Avenir program”. Also thanks to all the subjects and to INSEAD lab for their expertise in participant recruitment and management which made the process extremely easier.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mouna Benchekroun .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Benchekroun, M., Istrate, D., Zalc, V., Lenne, D. (2023). A Multi-Modal Dataset (MMSD) for Acute Stress Bio-Markers. In: Roque, A.C.A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2022. Communications in Computer and Information Science, vol 1814. Springer, Cham. https://doi.org/10.1007/978-3-031-38854-5_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-38854-5_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-38853-8

  • Online ISBN: 978-3-031-38854-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics