Abstract
Identifying the emotion that someone is feeling will allow to improve the experience of the person interaction with environments, devices, and contents. Our body responds to events around us, by emotional responses, reflected in cognitive, behavioral and physiological dimensions. In the present work, we target the electrocardiogram (ECG) response as a mean to express emotions. Its processing is performed using information-theoretical measures, allowing true exploratory data mining. Participants recruited for the experiment watched three video sets in three different days, with a different emotion being induced in each day: fear, happiness, and neutral condition. The method is divided in: (1) conversion of the real-valued ECG record into a symbolic time-series; (2) relative compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as a reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. An accuracy of 90% was obtained. A posteriori analysis of the false negative results indicated that there was a relation between the relative dissimilarity measure and the self-reported emotions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dolan, R.J.: Emotion, cognition, and behavior. Science 298(5596), 1191–1194 (2002)
Mauss, I.B., Robinson, M.D.: Measures of emotion: a review. Cogn. Emot. 23(2), 209–237 (2009)
Calvo, R., D’Mello, S.: Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1(1), 18–37 (2010)
Agrafioti, F., Hatzinakos, D., Anderson, A.K.: ECG pattern analysis for emotion detection. IEEE Trans. Affect. Comput. 3(1), 102–115 (2012)
Barrett, L.F., Mesquita, B., Ochsner, K.N., Gross, J.J.: The experience of emotion. Annu. Rev. Psychol. 58, 373–403 (2007)
Janssen, J.H., Tacken, P., de Vries, J.J.G., van den Broek, E.L., Westerink, J.H.D.M., Haselager, P., IJsselsteijn, W.A.: Machines outperform laypersons in recognizing emotions elicited by autobiographical recollection. Hum. Comput. Interact. 28(6), 479–517 (2013)
Ferreira, J., Brás, S., Silva, C.F., Soares, S.C.: An automatic classifier of emotions built from entropy of noise. Psychophysiology 54(4), 620–627 (2017)
Brás, S., Ferreira, J., Soares, S., Pinho, A.: Biometric and emotion identification: an ECG compression based method. Frontiers Psychol. 9 (2018)
Agrafioti, F., Hatzinakos, D.: ECG biometric analysis in cardiac irregularity conditions. SIViP 3(4), 329–343 (2009)
Odinaka, I., Lai, P.-H., Kaplan, A.D., O’Sullivan, J.A., Sirevaag, E.J., Rohrbaugh, J.W.: ECG biometric recognition: a comparative analysis. IEEE Trans. Inf. Forensics Secur. 7(6), 1812–1824 (2012)
Li, M., Vitányi, P.M.B.: An Introduction to Kolmogorov Complexity and Its Applications. Springer Science & Business Media, New York (2009)
Pinho, A.J., Ferreira, P.J.S.G., Neves, A.J.R., Bastos, C.A.C.: On the representability of complete genomes by multiple competing finite-context (Markov) models. PLoS ONE 6(6), e21588 (2011)
Berntson, G.G., Quigley, K.S., Lozano, D.: Cardiovascular psychophysiology. In: Handbook of Psychophysiology, 3rd edn., pp. 182–210. Cambridge University Press (2007)
Pinho, A.J., Ferreira, P.J.S.G.: Image similarity using the normalized compression distance based on finite context models. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 1993–1996. IEEE (2011)
Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11. ACM (2003)
Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: Proceedings of the 2nd Workshop on Temporal Data Mining, pp. 53–68 (2002)
Acknowledgments
This work is funded by national funds, European Regional Development Fund, FSE through COMPETE2020, through FCT, in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19; and in the scope of the projects UID/CEC/00127/2013 (IEETA/UA), CMUP-ERI/FIA/0031/2013 (VR2Market). J. M. Carvalho acknowledges the Doctoral Grant from FCT, ref. SFRH/BD/136815/2018.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
None declared.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Brás, S., Carvalho, J.M., Barros, F., Figueiredo, C., Soares, S.C., Pinho, A.J. (2020). An Information-Theoretical Method for Emotion Classification. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-31635-8_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31634-1
Online ISBN: 978-3-030-31635-8
eBook Packages: EngineeringEngineering (R0)