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A Study on the Effects of Sympathetic Skin Response Parameters in Diagnosis of Fibromyalgia Using Artificial Neural Networks

  • Systems-Level Quality Improvement
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Abstract

Fibromyalgia syndrome (FMS), usually observed commonly in females over age 30, is a rheumatic disease accompanied by extensive chronic pain. In the diagnosis of the disease non-objective psychological tests and physiological tests and laboratory test results are evaluated and clinical experiences stand out. However, these tests are insufficient in differentiating FMS with similar diseases that demonstrate symptoms of extensive pain. Thus, objective tests that would help the diagnosis are needed. This study analyzes the effect of sympathetic skin response (SSR) parameters on the auxiliary tests used in FMS diagnosis, the laboratory tests and physiological tests. The study was conducted in Suleyman Demirel University, Faculty of Medicine, Physical Medicine and Rehabilitation Clinic in Turkey with 60 patients diagnosed with FMS for the first time and a control group of 30 healthy individuals. In the study all participants underwent laboratory tests (blood tests), certain physiological tests (pulsation, skin temperature, respiration) and SSR measurements. The test data and SSR parameters obtained were classified using artificial neural network (ANN). Finally, in the ANN framework, where only laboratory and physiological test results were used as input, a simulation result of 96.51 % was obtained, which demonstrated diagnostic accuracy. This data, with the addition of SSR parameter values obtained increased to 97.67 %. This result including SSR parameters – meaning a higher diagnostic accuracy – demonstrated that SSR could be a new auxillary diagnostic method that could be used in the diagnosis of FMS.

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References

  1. Boissevain, M.D., and McCain, G.A., Toward an integrated understanding of fibromyalgia syndrome. I. Medical and Pathophysiological Aspects. Pain. 45(3):227–238, 1991. doi:10.1016/0304-3959(91)90047-2.

    CAS  PubMed  Google Scholar 

  2. Wolfe, F., Clauw, D.J., Fitzcharles, M.A., Goldenberg, D.L., Katz, R.S., Mease, P., Russell, A.S., Russell, I.J., Winfield, J.B., and Yunus, M.B., The American college of rheumatology preliminary diagnostic criteria for fibromyalgia and measurement of symptom severity. Arthritis Care Res (Hoboken). 62(5):600–610, 2010. doi:10.1002/acr.20140.

    Article  Google Scholar 

  3. Ozkan, O., Yildiz, M., Bilgin, S., and Koklukaya, E., Measuring the sympathetic skin response on body and using as diagnosis-purposed for lung cancer patients by artificial neural networks. J Med Syst. 34(3):407–412, 2010. doi:10.1007/s10916-009-9253-1.

    Article  PubMed  Google Scholar 

  4. Ulas, U.H., Unlu, E., Hamamcioglu, K., Odabasi, Z., Cakci, A., and Vural, O., Dysautonomia in fibromyalgia syndrome: sympathetic skin responses and RR interval analysis. Rheumatol Int. 26(5):383–387, 2006. doi:10.1007/s00296-005-0007-1.

    Article  PubMed  Google Scholar 

  5. Yildiz, M., Ozkan, O., and Koklukaya, E., Investigating the effect of sympathetic skin response parameters on the psychological test scores in patients with fibromyalgia syndrome by using ANNs. SAU J. Sci. 17(1):27–32, 2013.

    Google Scholar 

  6. Ozgocmen, S., Yoldas, T., Yigiter, R., Kaya, A., and Ardicoglu, O., R-R interval variation and sympathetic skin response in fibromyalgia. Arch Med Res. 37(5):630–634, 2006. doi:10.1016/j.arcmed.2005.11.008.

    Article  PubMed  Google Scholar 

  7. Tarvainen, M.P., Koistinen, A.S., Valkonen-Korhonen, M., Partanen, J., and Karjalainen, P.A., Analysis of galvanic skin responses with principal components and clustering techniques. IEEE Trans Biomed Eng. 48(10):1071–1079, 2001.

    Article  CAS  PubMed  Google Scholar 

  8. Tarvainen, M.P., Partanen, J., Karjalainen, P.A., Koistinen, A.S., and Valkonen-Korhonen, M., Principal component analysis of galvanic skin responses. Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual International Conference of the IEEE 4:3011–3014, 2000. doi:10.1109/IEMBS.2000.901513.

    Google Scholar 

  9. Everhart, D.E., and Harrison, D.W., Heart rate and fluency performance among high- and low-anxious men following autonomic stress. Int J Neurosci. 112(10):1149–1171, 2002. doi:10.1080/00207450290026120.

    Article  PubMed  Google Scholar 

  10. Ahuja ND, Agarwal AK, Mahajan NM, Mehta NH, Kapadia HN (2003) GSR and HRV: Its application in clinical diagnosis. Computer-Based Medical Systems, 2003. Proceedings. 16th IEEE Symposium 1:279–283. doi:10.1109/CBMS.2003.1212802

  11. Ozkan, O., Yildiz, M., and Koklukaya, E., The correlation of laboratory tests and sympathetic skin response parameters by using artificial neural networks in fibromyalgia patients. J Med Syst. 36(3):1841–1848, 2012. doi:10.1007/s10916-010-9643-4.

    Article  PubMed  Google Scholar 

  12. Berger, M.J., Hubli, M., and Krassioukov, A.V., Sympathetic skin responses and autonomic dysfunction in spinal cord injury. J Neurotrauma. 31(18):1531–1539, 2014. doi:10.1089/neu.2014.3373.

    Article  PubMed  Google Scholar 

  13. Saba, E., and Sultan, H., Autonomic nervous system changes associated with rheumatoid arthritis: clinical and electrophysiological study. The Egyptian Rheumatologist. 36(4):157–163, 2014. doi:10.1016/j.ejr.2014.03.002.

    Article  Google Scholar 

  14. Dag, Z.O., Alpua, M., Turkel, Y., and Isik, Y., Autonomic dysfunction in patients with polycystic ovary syndrome. Taiwanese Journal of Obstetrics & Gynecology. 54(4):381–384, 2015. doi:10.1016/j.tjog.2015.03.002.

    Article  Google Scholar 

  15. Ozkan, O., Yildiz, M., and Koklukaya, E., Improving diagnostic accuracy by supporting the laboratory tests which used for diagnosis of fibromyalgia syndrome with the sympathetic skin response parameters. SAU J. Sci. 15(1):1–7, 2011.

    Google Scholar 

  16. Zhai, J., Barreto, A.B., Chin, C., and Li, C., Realization of stress detection using psychophysiological signals for improvement of human-computer interactions”. Proceedings of the IEEE Southeastcon. 1:415–420, 2005. doi:10.1109/SECON.2005.1423280.

    Google Scholar 

  17. Evcik, D., Kizilay, B., and Gokcen, E., The effects of balneotherapy on fibromyalgia patients. Rheumatol Int. 22(2):56–59, 2002. doi:10.1007/s00296-002-0189-8.

    Article  PubMed  Google Scholar 

  18. Oztemel, E., Yapay sinir aglari. Papatya Publishing, Istanbul, 2012.

    Google Scholar 

  19. Askin, D., Iskender, I., and Mamizadeh, A., Dry type transformer winding thermal analysis using different neural network methods. J. Fac. Eng. Arch. Gazi Univ. 26(4):905–913, 2011.

    Google Scholar 

  20. Altun, A.A., and Allahverdi, N., A new approach to recognition of fingerprints enhanced by filtering techniques with artificial neural networks. J. Fac. Eng. Arch. Gazi Univ. 22(2):227–236, 2007.

    Google Scholar 

  21. MATLAB® Documentation Neural Network Toolbox Help (2015) http://www.mathworks.com/help/nnet/index.html. Accessed 26 Feb. 2015

  22. Oz C, Koker R, Cakar S (2002) Yapay Sinir Agları ile Karakter Tabanlı Plaka Tanıma. Elektrik – Elektronik – Bilgisayar Mühendisligi Sempozyumu (ELECO), Bursa

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Acknowledgments

This research is funded by The Scientific and Technological Research Council of Turkey (TUBITAK) and Sakarya University (Project code: 2009-50-02-016) within the context of the project code: 108E036, and titled “Evaluation of HRV, SSR and Psychological Tests using Wavelet Transform and Artificial Neural Nets for the diagnosis of Fibromyalgia Syndrome and Determination of Relations”. Relevant reference number of ethic committee report is B.30.2.SDU.0.01.00.01.301.01/19-271.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, 54187, Sakarya, Turkey

    Ozhan Ozkan, Murat Yildiz & Evren Arslan

  2. Physical Medicine and Rehabilitation Clinic, Egirdir Bone & Joint Disease Treatment and Rehabilitation Hospital, Isparta, Turkey

    Sedat Yildiz

  3. Department of Electrical and Electronics Engineering, Faculty of Engineering, Akdeniz University, Antalya, Turkey

    Suleyman Bilgin

  4. Department of Physical Medicine and Rehabilitation, Faculty of Medicine, Yildirim Beyazit University, Ankara, Turkey

    Selami Akkus

  5. Department of Neurology, Faculty of Medicine, Suleyman Demirel University, Isparta, Turkey

    Hasan R. Koyuncuoglu

  6. Department of Electrical and Electronics Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey

    Etem Koklukaya

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  1. Ozhan Ozkan
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  2. Murat Yildiz
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  3. Evren Arslan
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  4. Sedat Yildiz
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  5. Suleyman Bilgin
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  7. Hasan R. Koyuncuoglu
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  8. Etem Koklukaya
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Correspondence to Evren Arslan.

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This article is part of the Topical Collection on Systems-Level Quality Improvement.

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Ozkan, O., Yildiz, M., Arslan, E. et al. A Study on the Effects of Sympathetic Skin Response Parameters in Diagnosis of Fibromyalgia Using Artificial Neural Networks. J Med Syst 40, 54 (2016). https://doi.org/10.1007/s10916-015-0406-0

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