Skip to main content
Download PDF
  • Research Article
  • Open access
  • Published:

Differences in photoplethysmography morphological features and feature time series between two opposite emotions: Happiness and sadness

Artery Research volume 18, pages 7–13 (2017)Cite this article

Abstract

It has been well established that change in emotion state is associated with the change in physiological signals. This paper aimed to investigate the differences of finger photoplethysmography (PPG) morphological features and feature time series between happiness and sadness emotion states. Fifty-three volunteers were enrolled. Finger PPG signals were recorded under two emotion states with a random measurement order (first happiness emotion measurement then sadness or reverse). Seven morphological features were extracted, including three temporal features (T, T1 and T2), three area features (A, A1 and A2) and one amplitude feature (Amp). Five variability indices from the 5-min feature time series were calculated, including two time-domain indices (SDNN and RMSSD) and three frequency-domain indices (LFn, HFn and LF/HF). Results showed that temporal features T2 and T were critical features for identifying the two emotion states since not only they themselves but also their three frequency-domain variability indices had significant differences between the two emotion states. For area features, only two frequency-domain variability indices of LFn and HFn for A1 feature time series reported significant differences. Amplitude feature Amp itself, as well as its variability indices, did not had significant differences between the two emotion states. These results indicated that temporal features were more sensitive to response to emotion change than area and amplitude features. Moreover, compared with time-domain variability indices, frequency-domain variability indices were more suitable for short-term 5-min time series analysis for exploring the inherent but slight change due to the emotion effect.

References

  1. Eyben F, Wöllmer M, Poitschke T, Schuller B, Blaschke C, Färber B, et al. Emotion on the roaddnecessity, acceptance, and feasibility of affective computing in the car. Adv Hum Comp Interact 2010;2010.

  2. Bonora A, Benuzzi F, Monti G, Mirandola L, Pugnaghi M, Nichelli P, et al. Recognition of emotions from faces and voices in medial temporal lobe epilepsy. Epilepsy Behav 2011;20: 648–54.

    Google Scholar 

  3. Hasrul M, Hariharan M, Yaacob S. Human affective (emotion) behaviour analysis using speech signals: a review. In: 2012 International Conference on Biomedical Engineering (ICoBE). IEEE; 2012. p. 217–22.

  4. Wu Y, Kita K, Matsumoto K. Three predictions are better than one: sentence multi-emotion analysis from different perspectives. IEEJ Trans Electr Electron Eng 2014;9:642–9.

    Google Scholar 

  5. Jang E-H, Park B-J, Kim S-H, Chung M-A, Park M-S, Sohn J-H. Emotion classification based on bio-signals emotion recognition using machine learning algorithms. In: 2014 International Conference on Information Science, Electronics and Electrical Engineering (ISEEE). IEEE; 2014. p. 1373–6.

  6. Drummond PD, Quah SH. The effect of expressing anger on cardiovascular reactivity and facial blood flow in Chinese and Caucasians. Psychophysiology 2001;38:190–6.

    Google Scholar 

  7. Kim J, André E. Emotion recognition based on physiological changes in music listening. IEEE Trans Pattern Anal Mach Intell 2008;30:2067–83.

    Google Scholar 

  8. Koelstra S, Patras I. Fusion of facial expressions and EEG for implicit affective tagging. Image Vis Comput 2013;31:164–74.

    Google Scholar 

  9. Kukolja D, Popović S, Horvat M, Kovač B, Cosić K. Comparative analysis of emotion estimation methods based on physiological measurements for real-time applications. Int J Hum Comp Stud 2014;72:717–27.

    Google Scholar 

  10. Takahashi K, Namikawa S-y, Hashimoto M. Computational emotion recognition using multimodal physiological signals: elicited using Japanese kanji words. In: 2012 35th International Conference on Telecommunications and Signal Processing (TSP). IEEE; 2012. p. 615–20.

  11. Hamdi H, Richard P, Suteau A, Allain P. Emotion assessment for affective computing based on physiological responses. In: 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE; 2012. p. 1–8.

  12. Picard RW, Vyzas E, Healey J. Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans Pattern Anal Mach Intell 2001;23:1175–91.

    Google Scholar 

  13. Kim KH, Bang S, Kim S. Emotion recognition system using short-term monitoring of physiological signals. Med Biol Eng Comput 2004;42:419–27.

    Google Scholar 

  14. Mandryk RL, Atkins MS. A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. Int J Hum Comp Stud 2007;65:329–47.

    Google Scholar 

  15. Murugappan M, Ramachandran N, Sazali Y. Classification of human emotion from EEG using discrete wavelet transform. J Biomed Sci Eng 2010;3:390.

    Google Scholar 

  16. Katsis CD, Katertsidis NS, Fotiadis DI. An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders. Biomed Signal Process Control 2011;6:261–8.

    Google Scholar 

  17. Chang C-Y, Chang C-W, Zheng J-Y, Chung P-C. Physiological emotion analysis using support vector regression. Neurocomputing 2013;122:79–87.

    Google Scholar 

  18. Park MW, Kim CJ, Hwang M, Lee EC. Individual emotion classification between happiness and sadness by analyzing photoplethysmography and skin temperature. In: 2013 Fourth World Congress on Software Engineering (WCSE). IEEE; 2013. p. 190–4.

  19. Tajadura-Jiménez A, Larsson P, Väljamäe A, Västfjäll D, Kleiner M. When room size matters: acoustic influences on emotional responses to sounds. Emotion 2010;10:416.

  20. Quintana DS, Guastella AJ, Outhred T, Hickie IB, Kemp AH. Heart rate variability is associated with emotion recognition: direct evidence for a relationship between the autonomic nervous system and social cognition. Int J Psychophysiol 2012; 86:168–72.

    Google Scholar 

  21. Lee H-M, Kim D-J, Yang H-K, Kim K-S, Lee J-W, Cha E-J, et al. Human sensibility evaluation using photoplethysmogram (PPG). In: 2009. CISIS’09. International Conference on Complex, Intelligent and Software Intensive Systems. IEEE; 2009. p. 149–53.

  22. Baumgartner T, Esslen M, Jäncke L. From emotion perception to emotion experience: emotions evoked by pictures and classical music. Int J Psychophysiol 2006;60:34–43.

    Google Scholar 

  23. Rioul O, Vetterli M. Wavelets and signal processing. IEEE Signal Process Mag 1991;8:14–38.

    Google Scholar 

  24. Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng 1985;32:230–6.

    Google Scholar 

  25. Pinna GD, Maestri R, Sanarico M. Effects of record length selection on the accuracy of spectral estimates of heart rate variability: a simulation study. IEEE Trans Biomed Eng 1996;43: 754–7.

    Google Scholar 

  26. Ghasemi A, Zahediasl S. Normality tests for statistical analysis: a guide for non-statisticians. Int J Endocrinol Metab 2012;10: 486–9.

    Google Scholar 

  27. Khalfa S, Roy M, Rainville P, Dalla Bella S, Peretz I. Role of tempo entrainment in psychophysiological differentiation of happy and sad music? Int J Psychophysiol 2008;68:17–26.

    Google Scholar 

  28. Britton JC, Taylor SF, Berridge KC, Mikels JA, Liberzon I. Differential subjective and psychophysiological responses to socially and nonsocially generated emotional stimuli. Emotion 2006;6:150.

    Google Scholar 

  29. Yousef Q, Reaz M, Ali MAM. The analysis of PPG morphology: investigating the effects of aging on arterial compliance. Meas Sci Rev 2012;12:266–71.

    Google Scholar 

  30. Chen X, Liu N, Huang Y, Yun F, Wang J, Li J. Using the multiparameter variability of photoplethysmographic signals to evaluate short-term cardiovascular regulation. J Clin Monit Comput 2015;29:605–12.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, 250061, Jinan, China

    Fei Li, Licai Yang, Hongyu Shi & Chengyu Liu

Authors
  1. Fei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Licai Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongyu Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chengyu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chengyu Liu.

Rights and permissions

This is an open access article distributed under the CC BY-NC license. https://doi.org/creativecommons.org/licenses/by/4.0/

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, F., Yang, L., Shi, H. et al. Differences in photoplethysmography morphological features and feature time series between two opposite emotions: Happiness and sadness. Artery Res 18, 7–13 (2017). https://doi.org/10.1016/j.artres.2017.02.003

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1016/j.artres.2017.02.003

Keywords

Artery Research

ISSN: 1876-4401

Contact us