Skip to main content

Abstract

Developed countries are experiencing a dramatic increase in population ageing. Moreover, it is well known that elderly prefer stay at their homes over other options, increasing this way the medical expenditures. This also involves a major impact on the social and economic balance of the countries. Consequently, an important number of works have been carried out to improve the elderly quality of life and reduce the healthcare costs. However, few efforts have been made in monitoring the mental and emotional states of the ageing adults. This paper introduces a new approach based on the dynamic quadratic entropy for distinguishing different arousal levels. 278 one-minute-length EEG recordings from Dataset for Emotion Analysis using Physiological Signals are used in this study. The results show a decreasing brain complexity under excitement stimuli in central and parietal areas. These findings could be useful to train an emotional system for recognizing some basics emotions.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Trustees, M.: Technical review panel on the medicare trustees report, Review of the Assumptions and Methods of the Medicare Trustees

    Google Scholar 

  2. Fernández-Caballero, A., Latorre, J.M., Pastor, J.M., Fernández-Sotos, A.: Improvement of the elderly quality of life and care through smart emotion regulation. In: Ambient Assisted Living and Daily Activities, pp. 348–355. Springer (2014)

    Google Scholar 

  3. United Nations Department of Economic, World population ageing 2009, vol. 295. United Nations Publications (2010)

    Google Scholar 

  4. World Health Organization, et al.: Global health and ageing

    Google Scholar 

  5. Pantelopoulos, A., Bourbakis, N.: A survey on wearable systems for monitoring and early diagnosis for the elderly. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews 1, 1–12 (2010)

    Article  Google Scholar 

  6. Kario, K., Yasui, N., Yokoi, H.: Ambulatory blood pressure monitoring for cardiovascular medicine. IEEE Engineering in Medicine and Biology Magazine 22(3), 81–88 (2003)

    Article  Google Scholar 

  7. Jovanov, E., Milenkovic, A., Otto, C., De Groen, P.C.: A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. Journal of NeuroEngineering and Rehabilitation 2(1), 6 (2005)

    Article  Google Scholar 

  8. Garth, C., Tirthankar, G., Renita, M., Craig, C.: Wireless body area networks for healthcare: A survey. International Journal of Ad hoc Sensor & Ubiquitous Computing 3(3), 1 (2012)

    Article  Google Scholar 

  9. Ullah, S., Kwak, K.S.: An ultra low-power and traffic-adaptive medium access control protocol for wireless body area network. Journal of Medical Systems 36(3), 1021–1030 (2012)

    Article  Google Scholar 

  10. Castillo, J.C., Castro-González, A., Fernández-Caballero, A., Latorre, J.M., Pastor, J.M., Fernández-Sotos, A., Salichs, M.A.: Software architecture for smart emotion recognition and regulation of the ageing adult, Cognitive Computation (in press)

    Google Scholar 

  11. Martínez-Rodrigo, A., Zangróniz, R., Pastor, J.M., Fernández-Caballero, A.: Arousal level classification in the ageing adult by measuring electrodermal skin conductivity. In: Ambient Intelligence for Health, pp. 213–223. Springer (2015)

    Google Scholar 

  12. Costa, Â., Castillo, J.C., Novais, P., Fernández-Caballero, A., Simoes, R.: Sensor-driven agenda for intelligent home care of the elderly. Expert Systems with Applications 39(15), 12192–12204 (2012)

    Article  Google Scholar 

  13. Koelstra, S., Mühl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: Deap: A database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing 3(1), 18–31 (2012)

    Article  Google Scholar 

  14. Nasoz, F., Lisetti, C.L., Alvarez, K., Finkelstein, N.: Emotion recognition from physiological signals for user modeling of affect. In: Proceedings of the 3rd Workshop on Affective and Attitude User Modelling, Pittsburgh, PA, USA, pp. 1–8 (2003)

    Google Scholar 

  15. Valenza, G., Lanata, A., Scilingo, E.P.: The role of nonlinear dynamics in affective valence and arousal recognition. IEEE Transactions on Affective Computing 3(2), 237–249 (2012)

    Article  Google Scholar 

  16. German, W.J.: The hypothalamus and central levels of autonomic function. The Yale Journal of Biology and Medicine 12(5), 602–603 (1940)

    Google Scholar 

  17. Hatamikia, S., Nasrabadi, A.: Recognition of emotional states induced by music videos based on nonlinear feature extraction and som classification. In: 21th Iranian Conference on Biomedical Engineering, pp. 333–337. IEEE (2014)

    Google Scholar 

  18. Akar, S.A., Kara, S., Agambayev, S., Bilgic, V.: Nonlinear analysis of eeg in major depression with fractal dimensions. In: 37th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, pp. 7410–7413. IEEE (2015)

    Google Scholar 

  19. Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology 278(6), 2039–2049 (2000)

    Google Scholar 

  20. Alcaraz, R., Abásolo, D., Hornero, R., Rieta, J.J.: Optimal parameters study for sample entropy-based atrial fibrillation organization analysis. Computer Methods and Programs in Biomedicine 99(1), 124–132 (2010)

    Article  Google Scholar 

  21. Lake, D.E., Moorman, J.R.: Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices. American Journal of Physiology-Heart and Circulatory Physiology 300(1), 319–325 (2011)

    Article  Google Scholar 

  22. Abásolo, D., Hornero, R., Espino, P., Poza, J., Sánchez, C.I., de la Rosa, R.: Analysis of regularity in the eeg background activity of alzheimer’s disease patients with approximate entropy. Clinical Neurophysiology 116(8), 1826–1834 (2005)

    Article  Google Scholar 

  23. Jirayucharoensak, S., Pan-Ngum, S., Israsena, P.: Eeg-based emotion recognition using deep learning network with principal component based covariate shift adaptation. The Scientific World Journal (2014)

    Google Scholar 

  24. Gupta, R., Falk, T.H.: Affective state characterization based on electroencephalography graph-theoretic features. In: 2015 7th International IEEE/EMBS Conference on Neural Engineering, pp. 577–580. IEEE (2015)

    Google Scholar 

  25. Hosseini, S.A., Naghibi-Sistani, M.B.: Classification of emotional stress using brain activity. In: Applied Biomedical Engineering, pp. 313–336. INTECH Open (2011)

    Google Scholar 

  26. Dolcos, F., Cabeza, R.: Event-related potentials of emotional memory: encoding pleasant, unpleasant, and neutral pictures. Cognitive, Affective, & Behavioral Neuroscience 2(3), 252–263 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arturo Martínez-Rodrigo .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Martínez-Rodrigo, A., García-Martínez, B., Alcaraz, R., Pastor, J.M., Fernández-Caballero, A. (2016). EEG Mapping for Arousal Level Quantification Using Dynamic Quadratic Entropy. In: Lindgren, H., et al. Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2016). ISAmI 2016. Advances in Intelligent Systems and Computing, vol 476. Springer, Cham. https://doi.org/10.1007/978-3-319-40114-0_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-40114-0_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40113-3

  • Online ISBN: 978-3-319-40114-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics