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Measuring the Sympathetic Skin Response on Body and Using as Diagnosis-Purposed for Lung Cancer Patients by Artificial Neural Networks

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Abstract

In this study, the points of Sympathetic skin response that can be measured from different zones on body of healthy and patient subjects are determined. The Sympathetic skin responses on these points are obtained using a measurement device that is called Grass Model 7 Polygraph 1. The database is formed in Cerrahpaşa University, Faculty of Medicine and data is taken from healthy and patient subjects who are volunteer. Some parameters of the subjects which are more effective on SSR such as height, weight, age must be chosen between the specific limits to obtain results more clearly. The symmetric points on human body are chosen for the measurement. After that, the Sympathetic skin response values which are measured from a human body are simulated and tested by using artificial neural network toolbox on Matlab software. The structure of the chosen neural network is a multilayer feedforward neural network. According to simulation results, the application method as diagnosis-purposed of the lung cancer patients is explained by using the differences related to these values on the skin.

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References

  1. Enchanted Learning, C., Jeananda, August 2008, Available at: http://www.EnchantedLearning.com

  2. 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. 26:383–387, 2006. doi:10.1007/s00296-005-0007-1.

    Article  Google Scholar 

  3. Lee, C., Yoo, S.K., Park, Y., Kim, N., Jeong, K., and Lee, B., Using Neural Network to Recognize Human Emotions from Heart Rate Variability and Skin Resistance, Proceedings of the 2005 IEEE, Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, September 1–4, 2005.

  4. Collins, K. J., Sweat Glands: Eccrineand Apocrine. In: Greaves, M. W., and Shuster, S. (Eds.), Handbook of Experimental Pharmacology. Berlin: Springer-Verlag, 1989, pp 193–212.

  5. Esen, F., Electrodermal activity. J Med Sci Vol:20, No:1, 2000.

  6. Kucera, P., Goldenberg, Z., and Kurca, E., Sympathetic skin response: Review of the method and its clinical use. Bratisl. Lek Listy (Tlacene Vyd). 105 (3)108–116, 2004.

    Google Scholar 

  7. Vertugno, R., Liguori, R., P. Cortelli, P. Montagna, Sympathetic Skin Response – Basic Mechanisms and Clinical Applications, Clinical Autonomic Research, Volume:13, No:4, August, 2003

  8. Shahani, B. T., Halperin, J. J., Boulu, P., and Cohen, J., Sympathetic skin response – A method of assessing unmyelinated axon dysfunction in peripheral neuropathies. J. Neurol. Neurosurg. Psychiatry. 47:536–542, 1984. doi:10.1136/jnnp.47.5.536.

    Article  Google Scholar 

  9. 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. doi:10.1109/10.951509.

    Article  Google Scholar 

  10. Prof. Dr. Ayşen Akıncı Tan, http://www.hacettepem.org.tr/makaleler.php?a=&b=14&mNo=88, 14.06.2003

  11. İ. Çetinyalçın, Cilt Rezistansına Tesir Eden Muhtelif Faktörler, Periferik Sinir Hastalıklarında Cilt Rezistansı ve Ehemmiyeti, Uzmanlık Tezi, İ.Ü. Cerrahpaşa Tıp Fakültesi Kütüphanesi, No: 538, 2000.

  12. Okuda-Matsuoka, H., Tanaru, T., Mano, Y., and Takayanagi, T., Sympathetic skin responses in the face by magnetic stimulation of the neck. Rinsho Shinkeigaku. 36 (10)1190–1192, 1996.

    Google Scholar 

  13. Backpropagation, W., The Free Encyclopedia, August, 2008, Available at: http://en.wikipedia.org/w/index.php?title=Backpropagation&oldid=233606899

  14. Bolat, S., Kalenderli, Ö., Levenberg-Marquardt Öğrenme Algoritması Kullanılan Yapay Sinir Ağı ile Elektrod Biçim Optimizasyonu, İ.T.Ü, International XII. Turkish Symposium on Artificial Intelligence and Neural Networks, 2003.

  15. Demuth, H., Beale, M., Neural Network Toolbox: For Use with MATLAB, User’s Guide Version 3.0, 1998.

  16. Levenberg–Marquardt algorithm, Wikipedia, The Free Encyclopedia, August, 2008, Available at: http://en.wikipedia.org/w/index.php?title=Levenberg%E2%80%93Marquardt_algorithm&oldid=233323587

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Authors and Affiliations

  1. Department of Electrical-Electronics Engineering, Sakarya University, Esentepe Campus, 54187, Sakarya, Turkey

    Özhan Özkan, Murat Yildiz & Etem Köklükaya

  2. Golhisar Vocational School of Higher Education, Mehmet Akif Ersoy University, Burdur, Turkey

    Süleyman Bilgin

Authors
  1. Özhan Özkan
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  2. Murat Yildiz
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  3. Süleyman Bilgin
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  4. Etem Köklükaya
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Correspondence to Özhan Özkan.

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Özkan, Ö., Yildiz, M., Bilgin, S. et al. Measuring the Sympathetic Skin Response on Body and Using as Diagnosis-Purposed for Lung Cancer Patients by Artificial Neural Networks. J Med Syst 34, 407–412 (2010). https://doi.org/10.1007/s10916-009-9253-1

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  • DOI: https://doi.org/10.1007/s10916-009-9253-1

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