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FPGA Synthesis of SIRM Fuzzy System-Classification of Diabetic Epilepsy Risk Levels from EEG Signal Parameters and CBF

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 221)

Abstract

Now a day the epilepsy risk level classification is one of the most important thing in diabetic patient’s treatment. That risk level classification is proposed in this paper. SIRM fuzzy processor is synthesized through the FPGA. FUZZY rules are the best way to deal with natural conditions. In this paper epilepsy classification is performed through minimum no of rules. Cerebral blood flow level and EEG signals are used as input parameters. SIRM fuzzy processor with the tuned and untuned conditions is checked for various input values. The better the fuzzy system is identified based on performance and quality values. The tuned SIRM system with five rules is selected which has the performance of 98.58 % and quality value of 36.56. The SIRM system is simulates through VHDL and synthesized by FPGA which has performance value of 98.28. This SIRM fuzzy model is compared with other techniques like homogeneous system, heterogeneous system.

Keywords

Epilepsy risk level Cerebral blood flow VLSI design and simulation SIRM fuzzy system 

References

  1. 1.
    Harikumar R, Selvan S (2002) Fuzzy based classification of patient state in diabetic neuropathy using cerebral blood flow. J Syst Soc India Paritantra 7(1):37–41Google Scholar
  2. 2.
    Harikumar R, Selvan S (1997) Analysis of cerebral blood flow in diabetic Neuropathy using impedance Technique. In: Proceedings Of NCBME’97 Anna University, Chennai, pp 3.11–3.14Google Scholar
  3. 3.
    Clement C, Pang et al. (2003) A comparison of algorithms for detection of spikes in the EEG. IEEE Trans Biomed Eng 50(4):521–526Google Scholar
  4. 4.
    Adlassnig KA (1986) Fuzzy set theory in medical diagnosis. IEEE Trans Syst Man Cybern 16(3):260–265CrossRefGoogle Scholar
  5. 5.
    Muro et al. (1989) A mathematical model of cerebral blood flow chemical regulation -part II. IEEE Trans Biomed Eng 36(2):192–201Google Scholar
  6. 6.
    Yager’s RR (1998) On ordered weighted averaging aggregation operators in multi criteria decision making. IEEE Trans Syst Man Cybern 18(1):183–190CrossRefGoogle Scholar
  7. 7.
    Harikumar R, Sukanesh R, Sabarish Narayanan B (2003) Application of aggregation operators in fuzzy logic based Classification Of diabetic epilepsy risk level. In: Proceedings of annual convention and exhibition (ACE) IEEE India Council ACE, PuneGoogle Scholar
  8. 8.
    Paramasivam K, Harikumar R, Gunavathi K (2003) Simulation of VLSI design using parallel architecture for epilepsy risk level diagnosis in diabetic neuropathy. In: Proceedings of national conference on VLSI design and testing, Coimbatore, India, Febuary 21st and 22ndGoogle Scholar
  9. 9.
    Yi JQ et al. (2002) A proposal of SIRMs dynamically connected fuzzy inference model for plural input fuzzy control. Fuzzy Sets Syst 125(1):79–92)Google Scholar
  10. 10.
    Sukanesh R, Harikumar R, Shanmugam Jothi M (2009) FPGA synthesis of heterogeneous and SIRM fuzzy system for classification of diabetic epilepsy risk levels. IE India, 90Google Scholar
  11. 11.
    Leo P, Karall MdJoslin (1989) Diabetes manual, LEA and FEBIGER. Philadelphia London, Chapter 16Google Scholar
  12. 12.
    Mathews JNS et al (1991) Statistical method for the estimation of cerebral blood flow using the kety-schmidt technique. Clin Sci 97:485–492CrossRefGoogle Scholar
  13. 13.
    Gayton AC (1996) Textbook of medical physiology: prism books Private Limited. 9th edn. BangaloreGoogle Scholar
  14. 14.
    Alison A, Dingle et al. (1993) A multi stage system to detect epileptic from activity in the EEG. IEEE Trans Biomed Eng 40(12):1260–1268Google Scholar
  15. 15.
    Verilog HDL Language Reference Manual. IEEE (2001)Google Scholar
  16. 16.
    Mahamoud A, Jayabharathi MD (1995) FPGA for fuzzy controllers. IEEE Tran Syst Man Cybern 25(1)Google Scholar
  17. 17.
    Xilinx ISE web pack (2009)Google Scholar
  18. 18.
    Basic concepts from en.wikipedia.org/wiki/Very-large- scale integrationGoogle Scholar
  19. 19.
    Kim YD, Hyung LK (1997) High-speed flexible fuzzy hardware for fuzzy information processing. IEEE Trans Syst Man Cybern A 27:45–56Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of ECEThiagarajar College of EngineeringMaduraiIndia
  2. 2.Department of ITK.L.N. College of EngineeringMaduraiIndia
  3. 3.Department of ECEBannari Amman Institute of TechnologySathyamangalamIndia

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