JACIII Vol.13 No.4 pp. 352-359
doi: 10.20965/jaciii.2009.p0352


An Application of Rough Set Analysis toa Psycho-Physiological Study – Assessing the RelationBetween Psychological Scale and Immunological Biomarker

Shusaku Nomura* and Yasuo Kudo**

*Top Runner Incubation Center for Academia-Industry Fusion, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, 940-2188, Japan

**Department of Computer Science and Systems Engineering, Muroran Institute of Technology, 27-1 Mizumoto, Muroran, 050-8585, Japan

November 25, 2008
March 3, 2009
July 20, 2009
rough sets theory, stress, biomarker, POMS, immunoglobulin A

This study aims at an application of rough set theory to illustrate the relationship between human psychological and physiological states. Recent behavioral medicine studies have revealed that various human secretory substances change according to mental states. These substances, the hormones and immune substances, show temporal increase against mental stress. Thus, it is frequently introduced as biomarkers of mental stress. The relationship between these biomarkers and human chronic stresses or daily mental states was also suggested in the previous studies. However the results of these studies were sometimes inconsistent with each other. Some technical reasons were indicated for this discrepancy. Among that, we focused on the analysis technique investigating the relationship between human psychological state, i.e., scores of a psychological scale, and physiological state, i.e., level of the secretory biomarkers. In this paper, we introduced Rough Set analysis method instead of using a conventional linear correlation analysis method. In the experiment, the salivary secretory immunoglobulin A (IgA), which is a major stress biomarker, of 20 male students was assessed before and after a short-term stressful mental workload. Also, 65 items of psychological mood scale was assessed as a psychological index. The result showed that some items strongly related with the change in the IgA, while no significant linear correlation was obtained among them.

Cite this article as:
S. Nomura and Y. Kudo, “An Application of Rough Set Analysis toa Psycho-Physiological Study – Assessing the RelationBetween Psychological Scale and Immunological Biomarker,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.4, pp. 352-359, 2009.
Data files:
  1. [1] Z. Pawlak, “Rough Sets,” International Journal of Computer and Information Science, Vol.11, pp. 341-356, 1982.
  2. [2] Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning about Data,” Kluwer Academic Publisher, 1991.
  3. [3] H. B. Valdimarsdottir and A. A. Stone, “Psychosocial factors and secretory immunoglobulin A,” Critical reviews in oral biology and medicine, Vol.8, pp. 461-474, 1997.
  4. [4] R. Ader, L. Felten, and N. Cohen, N. (Eds.), “Psychoneruoimmunology,” 3rd ed., Academic Press, New York, 2001.
  5. [5] J. A. Bosch, C. Ring, E. J. de Geus, E. C. Veerman, and A. V. Amerongen, “Stress and secretory immunity,” International Review of Neurobiology, Vol.52, pp. 213-253, 2002.
  6. [6] S. Wakida, Y. Tanaka and H. Nagai, “High throughput screening for stress marker,” Bunseki, pp. 309-316, 2004 (in Japanese).
  7. [7] D. M. MacNair, M. Lorr, and L. F. Droppleman, “Profile of mood states revised,” Educational and Institutional Testin Service, San Diego, 1992.
  8. [8] S. Tsujita and K. Morimoto, “Secretory IgA in Saliva can be a Useful Stress Maker,” Environmental Health and Preventive Medicine, Vol.4, pp. 1-8, 1999.
  9. [9] J. B. Jemmott III and D. C. McClelland, “Secretory IgA as a measure of resistance to infectious disease: comments on Stone, Cox, Valdimarsdottir, and Neale,” Behavioral Medicine, Vol.15, pp. 63-71, 1989.
  10. [10] R. I. Gregory, D. E. Kim, J. C. Kindle, L. C. Hobbs, and D. R. Lloyd, “Immunoglobulin-degrading enzymes in localized juvenile periodontitis,” Journal of periodontal research, Vol.27, pp. 176-183, 1992.
  11. [11] A. A. Stone, J. M. Neale, D. S. Cox, A. Napoli, H. Valdimarsdottir, and E. Kennedy-Moore, “Daily events are associated with a secretory immune response to an oral antigen in men,” Health Psychology, Vol.13, pp. 440-446, 1994.
  12. [12] R. A. Martin and J. P. Dobbin, “Sense of humor, hassles, and immunoglobulin A: evidence for a stress-moderating effect of humor,” International journal of psychiatry in medicine, Vol.18, pp. 93-105, 1988.
  13. [13] R. B. Martin, C. A. Guthrie, and C. G. Pitts, “Emotional crying, depressed mood, and secretory immunoglobulin A,” Behavioral Medicine, Vol.19, pp. 111-114, 1993.
  14. [14] J. B. Jemmott III, J. Z. Borysenko, M. Borysenko, D. C. McClelland, R. Chapman, D. Meyer, and H. Benson, “Academic stress, power motivation, and decrease in secretion rate of salivary secretory immunoglobulin A,” Lancet, 1(8339), pp. 1400-1402, 1983.
  15. [15] P. Evans, M. Bristow, F. Hucklebridge, A. Clow, and F. Y. Pang, “Stress, arousal, cortisol and secretory immunoglobulin A in students undergoing assessment,” The British journal of clinical psychology, Vol.33, pp. 575-576, 1994.
  16. [16] R. G. Green and M. L. Green, “Relaxation increases salivary immunoglobulin A1,” Psychological Reports, Vol.61, pp. 623-629, 1987.
  17. [17] W. E. Knight and N. S. Rickard, “Relaxing Music Prevents Stress-Induced Increase in Subjective Anxiety, Systolic Blood Pressure, and Heart Rate in Healthy Males and Females,” Journal of Music Therapy, Vol.38, pp. 254-272, 2001.
  18. [18] K. Yokoyama and S. Araki, “Nihongo ban POMS tebiki (the guide of profile of mood states Japanese version),” 5th ed., Kaneko Shobo, Tokyo, 1993.
  19. [19] J. B. Jemmott III and K. Magloire K, “Academic stress, social support, and secretory immunoglobulin A,” Journal of Personality and Social Psychology, Vol.55, No.5, pp. 803-810, 1988.
  20. [20] H. Ohira, “Social support and salivary secretory immunoglobulin A response in women to stress of making a public speech,” Perceptual and Motor Skills, Vol.98, pp. 1241-1250, 2004.
  21. [21] Y. Kudo and S. Nomura, “Rough Set Analysis on the Relation Between Human Psychological Mood State, and the Immune Function,” Proc. of 2008 IEEE Conf. on Soft Computing in Industrial Applications, pp. 228-233, 2008.
  22. [22] N. Mori, H. Tanaka and K. Inoue (Eds.), “Rough Sets and Kansei: Knowledge Acquisition and Reasoning from Kansei Data,” Kaibundo, 2004 (in Japanese).
  23. [23] L. Polkowski, “Rough Sets: Mathematical Foundations,” Advances in Soft Computing, Physica-Verlag, 2002.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Nov. 18, 2019