Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction

Hany Ferdinando, Tapio Seppänen, Esko Alasaarela

2017

Abstract

Dimensionality reduction (DR) is an important issue in classification and pattern recognition process. Using features with lower dimensionality helps the machine learning algorithms work more efficient. Besides, it also can improve the performance of the system. This paper explores supervised dimensionality reduction, LDA (Linear Discriminant Analysis), NCA (Neighbourhood Components Analysis), and MCML (Maximally Collapsing Metric Learning), in emotion recognition based on ECG signals from the Mahnob-HCI database. It is a 3-class problem of valence and arousal. Features for kNN (k-nearest neighbour) are based on statistical distribution of dominant frequencies after applying a bivariate empirical mode decomposition. The results were validated using 10-fold cross and LOSO (leave-one-subject-out) validations. Among LDA, NCA, and MCML, the NCA outperformed the other methods. The experiments showed that the accuracy for valence was improved from 55.8% to 64.1%, and for arousal from 59.7% to 66.1% using 10-fold cross validation after transforming the features with projection matrices from NCA. For LOSO validation, there is no significant improvement for valence while the improvement for arousal is significant, i.e. from 58.7% to 69.6%.

References

  1. Agrafioti, F., Hatzinakos, D. & Anderson, A. K., 2012. ECG Pattern Analysis for Emotion Detection. IEEE Transactions on Affective Computing, 3(1), pp. 102- 115.
  2. Ferdinando, H., Seppänen, T. & Alasaarela, E., 2016. Comparing Features from ECG Pattern and HRV Analysis for Emotion Recognition System. Chiang Mai, Thailand, The annual IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2016).
  3. Ferdinando, H., Ye, L., Seppänen, T. & Alasaarela, E., 2014. Emotion Recognition by Heart Rate Variability. Australian Journal of Basic and Applied Sciences, 8(14), pp. 50-55.
  4. Fratini, A., Sansone, M., Bifulco, P. & Cesarelli, M., 2015. Individual identification via electrocardiogram analysis. BioMedical Engineering OnLine, 14(78), pp. 1-23.
  5. Globerson, A. & Roweis, S., 2006. Metric Learning by Collapsing Classes. In: Y. Weiss & B. Schölkopf, eds. Advances in Neural Information Processing Systems 18. Cambridge, MA: MIT Press, p. 451-458.
  6. Goldberger, J., Roweis, S., Hinton, G. & Salakhutdinov, R., 2005. Neighborhood Components Analysis. In: L. K. Saul, Y. Weiss & L. Bottou, eds. Advances in Neural Information Processing System Vol. 17. Cambridge: MIT Press, p. 513-520.
  7. Jolliffe, I., 2002. Principal Component Analysis. 2 ed. New York: Springer Verlag.
  8. Labiak, J. & Livescu, K., 2011. Nearest Neighbors with Learned Distances for Phonetic Frame Classification. Florence, Italy., International Speech Communication Association (ISCA).
  9. Lee, J. A. & Verleysen, M., 2010. Unsupervised Dimensionality Reduction: Overview and Recent Advances. Barcelona, Spain, IEEE World Congress on Computational Intelligence (WCCI) 2010.
  10. McDuff, D. et al., 2012. AffectAura: an intelligent system for emotional memory. New York, Association for Computing Machinery (ACM).
  11. McSharry, P. E., Clifford, G. D., Tarassenko, L. & Smith, L. A., 2003. A Dynamical Model of Generating Synthetic Electrocardiogram Signals. IEEE Transactions on Biomedical Engineering, 50(3), pp. 289-294.
  12. Rilling, G., Flandrin, P., Gonçalves, P. & Lilly, J. M., 2007. Bivariate Empirical Mode Decomposition. IEEE Signal Processing Letters, 14(12), pp. 936-939.
  13. Romero, J., Diago, L., Shinoda, J. & Hagiwara, I., 2015. Comparison of Data Reduction Methods for the Analysis of Iyashi Expressions using Brain Signals. Journal of Advanced Simulation in Science and Engineering, 2(2), pp. 349-366.
  14. Sammon, J. W., 1969. A nonlinear mapping algorithm for data structure analysis. EEE Transactions on Computers, CC-18(5), pp. 401-409.
  15. Soleymani, M., Lichtenauer, J., Pun, T. & Pantic, M., 2012. A Multimodal Database for Affect Recognition and Implicit Tagging. IEEE Transactions on Affective Computing, 3(1), pp. 1-14.
  16. Sugiyama, M., 2007. Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis. Journal of Machine Learning Research, Volume 8, pp. 1027-1061.
  17. Valenzi, S., Islam, T., Jurica, P. & Cichocki, A., 2014. Individual Classification of Emotions Using EEG. Journal of Biomedical Science and Engineering, Volume 7, pp. 604-620.
  18. van der Maaten, L., 2016. Matlab Toolbox for Dimensionality Reduction - Laurens van der Maaten. [Online] Available at: https://lvdmaaten.github.io/drtoolbox/ [Accessed 28 7 2016].
  19. Weinberger, K. Q., Blitzer, J. & Saul, L. K., 2005. Distance Metric Learning for Large Margin Nearest Neighbor Classification. Advances in Neural Information Processing System, Volume 18, p. 1473-1480.
  20. Weinberger, K. Q. & Saul, L. K., 2009. Distance Metric Learning for Large Margin Nearest Neighbor Classification. Journal of Machine Learning Research, Volume 10, pp. 207-244.
  21. Zhang, S. & Zhao, X., 2013. Dimensionality reductionbased spoken emotion recognition. Multimedia Tools and Applications, 63(3), p. 615-646.
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Paper Citation


in Harvard Style

Ferdinando H., Seppänen T. and Alasaarela E. (2017). Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 112-118. DOI: 10.5220/0006147801120118


in Bibtex Style

@conference{icpram17,
author={Hany Ferdinando and Tapio Seppänen and Esko Alasaarela},
title={Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={112-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006147801120118},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction
SN - 978-989-758-222-6
AU - Ferdinando H.
AU - Seppänen T.
AU - Alasaarela E.
PY - 2017
SP - 112
EP - 118
DO - 10.5220/0006147801120118