Associative Memory Approach for the Diagnosis of Parkinson’s Disease

  • Elena Acevedo
  • Antonio Acevedo
  • Federico Felipe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)


A method for diagnosing Parkinson’s disease is presented. The proposal is based on associative approach, and we used this method for classifying patients with Parkinson’s disease and those who are completely healthy. In particular, Alpha-Beta Bidirectional Associative Memory is used together with the modified Johnson-Möbius codification in order to deal with mixed noise. We use three methods for testing the performance of our method: Leave-One-Out, Hold-Out and K-fold Cross Validation and the average obtained was of 97.17%.


Classification Associative Models Alpha-Beta BAM Codification 


  1. 1.
    Goetz, C.G.: Early Iconography of Parkinson’s Disease. In: Handbook of Parkinson’s Disease, 4th edn., Informa Healthcare, New York (2007)Google Scholar
  2. 2.
    Factor, S.A., Weiner, W.J.: Parkinson’s Disease: Diagnosis and Clinical Management, 2nd edn. Demos, New York (2008)Google Scholar
  3. 3.
    Lieberman, A.: 100 Questions and Answers about Parkinson Disease. Jones and Barttlet Publishers, Sudbury (2003)Google Scholar
  4. 4.
    Jancovic, J.: Parkinson’s disease: clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatry 79, 368–376 (2008)CrossRefGoogle Scholar
  5. 5.
    Adler, C.H., Ahlskog, J.E.: Parkinson’s Disease and Movements Disorders: Diagnosis and Treatment Guidelines for the Practicing Physician. Humana Press, New Jersey (2000)CrossRefGoogle Scholar
  6. 6.
    Unified Parkinson Disease Rating Scale (UPDRS),
  7. 7.
    World Health Organization,
  8. 8.
  9. 9.
    Acton, P.D., Newberg, A.: Artificial network classifier for the diagnosis of Parkinson´s disease using [99mTc]TRODAT-1 and SPECT. Phys. Med. Biol. 51(12), 3057–3066 (2006)CrossRefGoogle Scholar
  10. 10.
    Ericsson, A., Lonsdale, M.N., Astrom, K., Edenbrandt, L., Friberg, L.: Decision Support System for the Diagnosis of Parkinson’s Disease. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 740–749. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Yoshida, H., Nakagawa, K., Anai, H., Horimoto, K.: Exact Parameter Determination for Parkinson’s Disease Diagnosis with PET Using an Algebraic Approach. In: Anai, H., Horimoto, K., Kutsia, T. (eds.) Ab 2007. LNCS, vol. 4545, pp. 110–124. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Keijsers, N.L.W., Horstink, M.W.I.M., Gielen, C.C.A.M.: Automatic, unsupervised classification of dyskinesia in patients with Parkinson’s Disease. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, Springer, Heidelberg (2003)Google Scholar
  13. 13.
    Djurić-Jovičić, M., Jovičić, N.S., Milovanović, I., Radovanović, S., Kresojević, N., Popović, M.B.: Classification of walking patterns in Parkinson’s disease patients based on inertial sensor data. Neural Network Applications in Electrical Engineering (NEUREL), 3–6 (2010)Google Scholar
  14. 14.
    Parkinsons Telemonitoring Data Set,
  15. 15.
    Ene, M.: Neural network-based approach to discriminate healthy people from those with Parkinson’s disease. Annals of the University of Craiova, Math. Comp. Sci. Ser. 35, 112–116 (2008)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Sakar, O., Kursun, O.: Telediagnosis of Parkinson’s Disease Using Measurements of Disphonia. J. Med. Syst. 34, 591–599 (2010)CrossRefGoogle Scholar
  17. 17.
    Bhattacharya, I., Bhatia. M.P.S.: SVM classification to distinguish Parkinson disease patients. In: Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing A2CWiC 2010, India (2010)Google Scholar
  18. 18.
    Gil, D., Johnson, M.: Diagnosing Parkinson by using Artificial Neural Networks and Support Vector Machines. Global Journal of Computer Science and Technology 9(4), 63–71 (2009)Google Scholar
  19. 19.
    Acevedo, E., Yáñez, C., López, I.: Alpha-Beta Bidirectional Associative Memories: Theory and Applications. Neural Processing Letters 26, 1–40 (2007)CrossRefGoogle Scholar
  20. 20.
    Ritter, G.X., Sussner, P., Diaz de León, J.L.: Morphological Associative Memories. IEEE Transactions on Neural Networks 9, 281–293 (1998)CrossRefGoogle Scholar
  21. 21.
    Yáñez-Márquez, C.: Associative Memories Based on Order Relations and Binary Operators (in Spanish). PhD Thesis. Centro de Investigación en Computación, Mexico (2002)Google Scholar
  22. 22.
    Mano, M.: Diseño digital, 16–26, 292–294. Prentice Hall, Englewood Cliffs (2001)Google Scholar
  23. 23.
    Flores, R.: Johnson-Möbius modified code-based Alpha-Beta Associative Memories (in Spanish). MD thesis, Centro de Investigación en Computación, Mexico (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Elena Acevedo
    • 1
  • Antonio Acevedo
    • 1
  • Federico Felipe
    • 1
  1. 1.Escuela Superior de Ingeniería Mecánica y Eléctrica, IPNMexico CityMexico

Personalised recommendations