Physiological Signal Processing for Emotional Feature Extraction

Peng Wu, Dongmei Jiang, Hichem Sahli

2014

Abstract

This paper introduces new approaches of physiological signal processing prior to feature extraction from electrocardiogram (ECG) and electromyography (EMG). Firstly a new signal denoising approach based on the Empirical mode decomposition (EMD) is presented. The EMD can decompose the noisy signal into a number of Intrinsic Mode Functions (IMFs). The proposed algorithm estimates the noise level of each IMF. Experiments show that the proposed EMD-based method provides better denoising results compared to state-of-art. In addition, a real-time QRS detection approach is proposed to be directly applied on the noisy ECG signals. Moreover, an adaptive thresholding approach is employed for the EMG segmentation. Both approaches are validated using synthetic and real physiological data resulting in good performances.

References

  1. Agrafioti, F., Hatzinakos, D., and Anderson, A. K. (2012). Ecg pattern analysis for emotion detection. IEEE Transactions on Affective Computing, 3:102-115.
  2. Andrade, A. O., Nasuto, S., Kyberd, P., Sweeney-Reed, C. M., and Kanijn, F. R. V. (2006). Emg signal filtering based on empirical mode decomposition. Biomedical Signal Processing and Control, 1:44-55.
  3. Aoi, M., Kamijo, M., and Yoshida, H. (2011). Relationship between Facial Expression and Facial Electromyogram (f-EMG) Analysis in the Expression of Drowsiness. In International Conference on Biometrics and Kansei Engineering, pages 65-70.
  4. Behbahani, S. and Dabanloo, N. (2011). Detection of qrs complexes in the ecg signal using multiresolution wavelet and thresholding method. In Computing in Cardiology, 2011, pages 805-808.
  5. Blanco-Velasco, M., Weng, B., and Barner, K. E. (2008). Ecg signal denoising and baseline wander correction based on the empirical mode decomposition. Computers in Biology and Medicine, 38:1-13.
  6. Boudraa, A. O., Cexus, J. C., and Saidi, Z. (2005). Emdbased signal noise reduction. Signal Processing, 1:33- 37.
  7. Donoho, D. L. (1995). Denoising by soft-thresholding. IEEE Transactions on Information Theory, 41:613- 627.
  8. Donoho, D. L. and Johnstone, I. M. (1994). Ideal spatial adaption via wavelet shrinkage. Biometrika, 81:425- 455.
  9. Ghosh, P. K., Tsiartas, A., and Narayanan, S. (2011). Robust voice activity detection using long-term signal variability. Audio, Speech, and Language Processing, IEEE Transactions on.
  10. Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. (2000). Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation, 101(23):e215-e220.
  11. Guralnik, V. and Srivastava, J. (1999). Event detection from time series data. In Knowledge Discovery and Data Mining, pages 33-42.
  12. Hamedi, M., Salleh, S.-H., and Swee, T. T. (2011). Surface electromyography-based facial expression recognition in bi-polar configuration. Journal of Computer Science, 7(9):1407-1415.
  13. Huang, N. E., Shen, Z., and Long, S. R. (1999). A new view of nonlinear water waves: The hilbert spectrum 1. Annual review of fluid mechanics, 31(1):417-457.
  14. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., and Liu, H. H. (1998). The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences, 454:903-995.
  15. Jing-tian, T., Qing, Z., Yan, T., Bin, L., and Xiao-kai, Z. (2007). Hilbert-huang transform for ecg de-noising. In Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on, pages 664-667.
  16. Karagiannis, A. and Constantinou, P. (2009). Noise components identification in biomedical signals based on empirical mode decomposition. In IEEE EMBS International Conference on Information Technology Applications in Biomedicine, pages 1-4.
  17. Kim, J. (2007). Bimodal emotion recognition using speech and physiological changes. Robust speech recognition and understanding, pages 265-280.
  18. Kohler, B.-U., Hennig, C., and Orglmeister, R. (2002). The principles of software qrs detection. IEEE Engineering in Medicine and Biology Magazine, 21:42-57.
  19. Kreibig, S. D. (2010). Autonomic nervous system activity in emotion: A review. Biological psychology, 84(3):394-421.
  20. Kreibig, S. D., Wilhelm, F. H., Roth, W. T., and Gross, J. J. (2007). Cardiovascular, electrodermal, and respiratory response patterns to fear-and sadness-inducing films. Psychophysiology, 44(5):787-806.
  21. Moody, G. B. and Mark, R. G. (2001). The impact of the mit-bih arrhythmia database. Engineering in Medicine and Biology Magazine, IEEE, 20(3):45-50.
  22. O zgünen, K. T., C¸ elik, U., and Kurdak, S. S. (2010). Determination of an optimal threshold value for muscle activity detection in emg analysis. Journal of Sports Science and Medicine, 9:620-628.
  23. Pantelopoulos, A. and Bourbakis, N. (2008). A survey on wearable biosensor systems for health monitoring. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pages 4887-4890.
  24. Picard, R. W. (2000). Affective computing. MIT press.
  25. Sakhnov, K., Verteletskaya, E., and Simak, B. (2009). Approach for energy-based voice detector with adaptive scaling factor. IAENG Internat. J. Comput. Sci, 36(4).
  26. Terzano, M. G., Parrino, L., Sherieri, A., Chervin, R., Chokroverty, S., Guilleminault, C., Hirshkowitz, M., Mahowald, M., Moldofsky, H., Rosa, A., et al. (2001). Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (cap) in human sleep. Sleep medicine, 2(6):537-553.
  27. Tikkanen, P. E. (1999). Nonlinear wavelet and wavelet packet denoising of electrocardiogram signal. Biological Cybernetics, 80(4):259-267.
  28. Ü stündag?, M., Gökbulut, M., S¸ engür, A., and Ata, F. (2012). Denoising of weak ecg signals by using wavelet analysis and fuzzy thresholding. Network Modeling Analysis in Health Informatics and Bioinformatics, 1(4):135-140.
  29. Van Boxtel, A. (2010). Facial emg as a tool for inferring affective states. In Proceedings of Measuring Behavior 2010, pages 104-108.
  30. Van Gerven, S. and Xie, F. (1997). A comparative study of speech detection methods. In Eurospeech, volume 97.
  31. Verbraeck, F. (2012). Objectifying human facial expressions for clinical applications. Master's thesis, Vrije Universiteit Brussel, Belgium.
  32. Wu, Z. and Huang, N. E. (2004). A study of the characteristics of white noise using the empirical mode decomposition method. Proceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences, 460:1597-1611.
Download


Paper Citation


in Harvard Style

Wu P., Jiang D. and Sahli H. (2014). Physiological Signal Processing for Emotional Feature Extraction . In Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-006-2, pages 40-47. DOI: 10.5220/0004727500400047


in Bibtex Style

@conference{phycs14,
author={Peng Wu and Dongmei Jiang and Hichem Sahli},
title={Physiological Signal Processing for Emotional Feature Extraction},
booktitle={Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS,},
year={2014},
pages={40-47},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004727500400047},
isbn={978-989-758-006-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - Physiological Signal Processing for Emotional Feature Extraction
SN - 978-989-758-006-2
AU - Wu P.
AU - Jiang D.
AU - Sahli H.
PY - 2014
SP - 40
EP - 47
DO - 10.5220/0004727500400047