Morphological ECG Analysis for Attention Detection

Carlos Carreiras, André Lourenço, Helena Aidos, Hugo Plácido da Silva, Ana Fred

2013

Abstract

The electroencephalogram (EEG) signal, acquired on the scalp, has been extensively used to understand cognitive function, and in particular attention. However, this type of signal has several drawbacks in a context of Physiological Computing, being susceptible to noise and requiring the use of impractical head-mounted apparatuses, which impacts normal human-computer interaction. For these reasons, the electrocardiogram (ECG) has been proposed as an alternative source to assess emotion, which is also continuously available, and related with the psychophysiological state of the subject. In this paper we present a study focused on the morphological analysis of the ECG signal acquired from subjects performing a task demanding high levels of attention. The analysis is made using various unsupervised learning techniques, which are validated against evidence found in a previous study by our team, where EEG signals collected for the same task exhibit distinct patterns as the subjects progress in the task.

References

  1. Ahern, G. L. and Schwartz, G. E. (1985). Differential lateralization for positive and negative emotion in the human brain: Eeg spectral analysis. Neuropsychologia, 23(6):745-755.
  2. Aidos, H. and Fred, A. (2011). Hierarchical clustering with high order dissimilarities. In Proceedings of the 7th International Conference on Machine Learning and Data Mining (MLDM 2011), volume 6871 of Lecture Notes in Computer Science, pages 280-293, New York, USA.
  3. Aidos, H. and Fred, A. (2012). Statistical modeling of dissimilarity increments for d-dimensional data: Application in partitional clustering. Pattern Recognition, 45(9):3061-3071.
  4. Almeida, M., Bioucas-Dias, J., and Vigário, R. (2009). Source separation of phase-locked subspaces. In Proceedings of the International Conference on Independent Component Analysis and Signal Separation, volume 5441, pages 203-210.
  5. Aviezer, H., Trope, Y., and Todorov, A. (2012). Body cues, not facial expressions, discriminate between intense positive and negative emotions. Science, 338(6111):1225-1229.
  6. Belle, A., Hargraves, R. H., and Najarian, K. (2012). An automated optimal engagement and attention detection system using electrocardiogram. Computational and Mathematical Methods in Medicine, 2012.
  7. Belle, A., Ji, S.-Y., Ansari, S., Hakimzadeh, R., Ward, K., and Najarian, K. (2010). Frustration detection with electrocardiograph signal using wavelet transform. In IEEE International Conference on Biosciences (BIOSCIENCESWORLD), pages 91-94. IEEE.
  8. Ben-Hur, A., Elisseeff, A., and Guyon, I. (2002). A stability based method for discovering structure in clustered data. In Pacific Symposium on Biocomputing.
  9. Canento, F., Fred, A., Silva, H., Gamboa, H., and Lourenc¸o, A. (2011). Multimodal biosignal sensor data handling for emotion recognition. In Proceedings of the IEEE Sensors Conference.
  10. Canento, F., Lourenc¸o, A., Silva, H., Fred, A., and Raposo, N. (2013). On real time ECG algorithms for biometric applications. In Proceedings of the 6th Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS).
  11. Carreiras, C., Aidos, H., Silva, H., and Fred, A. (2013). Exploratory eeg analysis using clustering and phaselocking factor. In Proceedings of the 6th Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 2013).
  12. Coan, J. A. and Allen, J. J. (2007). Handbook of emotion elicitation and assessment. Oxford University Press, USA.
  13. Dom, B. E. (2001). An information-theoretic external cluster-validity measure. Technical Report IBM Research Report RJ 10219, IBM Research Report.
  14. Duarte, F., Duarte, J., Fred, A., and Rodrigues, M. (2011). Average cluster consistency for cluster ensemble selection. In Fred, A., Dietz, J., Liu, K., and Filipe, J., editors, Knowledge Discovery, Knowlege Engineering and Knowledge Management, volume 128 of Communications in Computer and Information Science, pages 133-148. Springer Berlin Heidelberg.
  15. Dubes, R. and Jain, A. (1979). Validity studies in clustering methodologies. Pattern Recognition, 11:235-254.
  16. Engelse, W. A. H. and Zeelenberg, C. (1979). A single scan algorithm for QRS-detection and feature extraction. Computers in Cardiology, 6:37-42.
  17. Epp, C., Lippold, M., and Mandryk, R. L. (2011). Identifying emotional states using keystroke dynamics. In Proceedings of the 2011 annual conference on Human factors in computing systems, pages 715-724. ACM.
  18. Fairclough, S. H. (2009). Fundamentals of physiological computing. Interacting with computers, 21(1):133- 145.
  19. Fowlkes, E. B. and Mallows, C. L. (1983). A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 78(383):553-569.
  20. Fred, A. (2001). Finding consistent clusters in data partitions. In Proceedings of the Second International Workshop on Multiple Classifier Systems, pages 309- 318, London, UK. Springer-Verlag.
  21. Fred, A. and Jain, A. (2002). Evidence accumulation clustering based on the k-means algorithm. Structural, syntactic, and statistical pattern recognition, pages 303-333.
  22. Fred, A. and Jain, A. K. (2005). Combining multiple clustering using evidence accumulation. IEEE Trans. Pattern Analysis and Machine Intelligence, 27(6):835- 850.
  23. Fred, A. and Leita˜o, J. (2003). A new cluster isolation criterion based on dissimilarity increments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(8):944-958.
  24. Fulton, J. (1999). Mensa book of total genius. Barnes & Noble Books.
  25. Gamboa, H., Silva, H., and Fred, A. (2007). HiMotion project. Technical report, Instituto Superior Técnico, Lisbon, Portugal.
  26. Ghosh, J. and Acharya, A. (2011). Cluster ensembles. WIREs Data Mining and Knowledge Discovery, 1(4):305-315.
  27. Halkidi, M., Batistakis, Y., and Vazirgiannis, M. (2001). On clustering validation techniques. Journal of Intelligent Information Systems, 17:107-145.
  28. Jain, A. K. and Dubes, R. C. (1988). Algorithms for Clustering Data. Prentice-Hall, Inc., Upper Saddle River, NJ, USA.
  29. Kuncheva, L. I. and Vetrov, D. P. (2006). Evaluation of stability of k-means cluster ensembles with respect to random initialization. IEEE Trans. Pattern Anal. Mach. Intell., 28(11):1798-1808.
  30. Levenson, R. W. (1992). Autonomic nervous system differences among emotions. Psychological science, 3(1):23-27.
  31. Lourenc¸o, A., Fred, A., and Jain, A. K. (2010). On the scalability of Evidence Accumulation Clustering. In Proc. 20th International Conference on Pattern Recognition (ICPR), Istanbul Turkey.
  32. Luo, P., Xiong, H., Zhan, G., Wu, J., and Shi, Z. (2009). Information-theoretic distance measures for clustering validation: Generalization and normalization. IEEE Trans. on Knowl. and Data Eng., 21(9):1249-1262.
  33. Mak, J. N. and Wolpaw, J. R. (2009). Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects. IEEE Reviews in Biomedical Engineering, 2:187-199.
  34. Malmivuo, J. and Plonsey, R. (1995). Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. Oxford University Press, USA.
  35. Medina, L. (2009). Identification of stress states from ECG signals using unsupervised learning methods. Master's thesis, Universidade Técnica de Lisboa, Instituto Superior Técnico.
  36. Meila?, M. (2007). Comparing clusterings-an information based distance. J. Multivar. Anal., 98(5):873-895.
  37. Murray, I. R. and Arnott, J. L. (1993). Toward the simulation of emotion in synthetic speech: A review of the literature on human vocal emotion. The Journal of the Acoustical Society of America, 93:1097.
  38. Pell, M. D., Jaywant, A., Monetta, L., and Kotz, S. A. (2011). Emotional speech processing: disentangling the effects of prosody and semantic cues. Cognition & Emotion, 25(5):834-853.
  39. Pfurtscheller, G. and Lopes da Silva, F. H. (1999). Eventrelated EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 110:1842 - 1857.
  40. Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66(336):pp. 846-850.
  41. Silva, H., Fred, A., Eusebio, S., Torrado, M., and Ouakinin, S. (2012). Feature extraction for psychophysiological load assessment in unconstrained scenarios. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 4784- 4787. IEEE.
  42. Silva, H., Lourenc¸o, A., Lourenc¸o, R., Leite, P., Coutinho, D., and Fred, A. (2011). Study and evaluation of a single differential sensor design based on electro-textile electrodes for ECG biometrics applications. In Proceedings of the IEEE Sensors Conference.
  43. Theodoridis, S. and Koutroumbas, K. (1999). Patern recognition. Academic Press.
  44. Vega-Pons, S. and Ruiz-Shulcloper, J. (2011). A survey of clustering ensemble algorithms. International Journal of Pattern Recognition and Artifical Intelligence (IJPRAI), 25(3):337-372.
  45. Wallace, D. L. (1983). A method for comparing two hierarchical clusterings: Comment. Journal of the American Statistical Association, 78(383):pp. 569-576.
  46. Zheng, W., Zhou, X., Zou, C., and Zhao, L. (2006). Facial expression recognition using kernel canonical correlation analysis (kcca). IEEE Transactions on Neural Networks, 17(1):233-238.
Download


Paper Citation


in Harvard Style

Carreiras C., Lourenço A., Aidos H., Plácido da Silva H. and Fred A. (2013). Morphological ECG Analysis for Attention Detection . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 381-390. DOI: 10.5220/0004554403810390


in Bibtex Style

@conference{ncta13,
author={Carlos Carreiras and André Lourenço and Helena Aidos and Hugo Plácido da Silva and Ana Fred},
title={Morphological ECG Analysis for Attention Detection},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)},
year={2013},
pages={381-390},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004554403810390},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)
TI - Morphological ECG Analysis for Attention Detection
SN - 978-989-8565-77-8
AU - Carreiras C.
AU - Lourenço A.
AU - Aidos H.
AU - Plácido da Silva H.
AU - Fred A.
PY - 2013
SP - 381
EP - 390
DO - 10.5220/0004554403810390