Convolutional Neural Networks Applied for Parkinson’s Disease Identification

  • Clayton R. Pereira
  • Danillo R. Pereira
  • Joao P. PapaEmail author
  • Gustavo H. Rosa
  • Xin-She Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9605)


Parkinson’s Disease (PD) is a chronic and progressive illness that affects hundreds of thousands of people worldwide. Although it is quite easy to identify someone affected by PD when the illness shows itself (e.g. tremors, slowness of movement and freezing-of-gait), most works have focused on studying the working mechanism of the disease in its very early stages. In such cases, drugs can be administered in order to increase the quality of life of the patients. Since the beginning, it is well-known that PD patients feature the micrography, which is related to muscle rigidity and tremors. As such, most exams to detect Parkinson’s Disease make use of handwritten assessment tools, where the individual is asked to perform some predefined tasks, such as drawing spirals and meanders on a template paper. Later, an expert analyses the drawings in order to classify the progressive of the disease. In this work, we are interested into aiding physicians in such task by means of machine learning techniques, which can learn proper information from digitized versions of the exams, and them recommending a probability of a given individual being affected by PD depending on its handwritten skills. Particularly, we are interested in deep learning techniques (i.e. Convolutional Neural Networks) due to their ability into learning features without human interaction. Additionally, we propose to fine-tune hyper-arameters of such techniques by means of meta-heuristic-based techniques, such as Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization.


Convolutional Neural Networks Parkinson’s Disease Machine learning Meta-heuristics 



The authors are grateful to FAPESP grants #2014/16250-9 and #2015/25739-4, as well as CNPq grant #306166/2014-3.


  1. 1.
    Parkinson, J.: An essay on the shaking palsy. J. Neuropsychiatry Clin. Neurosci. 20(4), 223–236 (1817)Google Scholar
  2. 2.
    Fundation, P.D.: Statistics on parkinson’s: Who has parkinson’s? (2016)., Accessed 15-July-2016
  3. 3.
    LeCun, Y., Bengio, Y.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  4. 4.
    Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  5. 5.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Salakhutdinov, R., Hinton, G.E.: An efficient learning procedure for deep boltzmann machines. Neural Comput. 24(8), 1967–2006 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Holzinger, A.: Interactive machine learning for health informatics: When do we need the human-in-the-loop? Brain Inf. 3Google Scholar
  8. 8.
    Holzinger, A., Plass, M., Holzinger, K., Crişan, G.C., Pintea, C.-M., Palade, V.: Towards interactive machine learning (iML): applying ant colony algorithms to solve the traveling salesman problem with the human-in-the-loop approach. In: Buccafurri, F., Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-ARES 2016. LNCS, vol. 9817, pp. 81–95. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-45507-5_6 CrossRefGoogle Scholar
  9. 9.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Bristol (2008)Google Scholar
  10. 10.
    Yang, X.S., Gandomi, A.H.: Bat algorithm: a novel approach for global engineering optimization. Eng. Computations 29(5), 464–483 (2012)CrossRefGoogle Scholar
  11. 11.
    Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
  12. 12.
    Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)CrossRefGoogle Scholar
  13. 13.
    Spadotto, A.A., Guido, R.C., Papa, J.P., Falcão, A.X.: Parkinson’s disease identification through optimum-path forest. In: IEEE International Conference of the Engineering in Medicine and Biology Society, pp. 6087–6090 (2010)Google Scholar
  14. 14.
    Papa, J.P., Falcão, A.X., Suzuki, C.T.N.: Supervised pattern classification based on optimum-path forest. Int. J. Imaging Systems Technol. 19(2), 120–131 (2009)CrossRefGoogle Scholar
  15. 15.
    Papa, J.P., Falcão, A.X., Albuquerque, V.H.C., Tavares, J.M.R.S.: Efficient supervised optimum-path forest classification for large datasets. Pattern Recogn. 45(1), 512–520 (2012)CrossRefGoogle Scholar
  16. 16.
    Spadotto, A.A., Guido, R.C., Carnevali, F.L., Pagnin, A.F., Falcão, A.X., Papa, J.P.: Improving parkinson’s disease identification through evolutionary-based feature selection. In: IEEE International Conference of the Engineering in Medicine and Biology Society, pp. 7857–7860 (2011)Google Scholar
  17. 17.
    Das, R.: A comparison of multiple classification methods for diagnosis of parkinson disease. Expert Syst. Appl. 37(2), 1568–1572 (2010)CrossRefGoogle Scholar
  18. 18.
    Weber, S.A.T., Santos Filho, C.A., Shelp, A.O., Rezende, L.A.L., Papa, J.P., Hook, C.: Classification of handwriting patterns in patients with parkinson’s disease, using a biometric sensor. Global Adv. Res. J. Med. Med. Sci. 11(3), 362–366 (2014)Google Scholar
  19. 19.
    Zhao, S., Rudzicz, F., Carvalho, L.G., Marquez-Chin, C., Livingstone, S.: Automatic detection of expressed emotion in parkinson’s disease. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4813–4817 (2014)Google Scholar
  20. 20.
    Tsanas, A., Little, M.A., McSharry, P.E., Spielman, J., Ramig, L.O.: Novel speech signal processing algorithms for high-accuracy classification of parkinson’s disease. IEEE Trans. Biomed. Eng. 59(5), 1264–1271 (2012)CrossRefGoogle Scholar
  21. 21.
    Harel, B., Cannizzaro, M., Snyder, P.J.: Variability in fundamental frequency during speech in prodromal and incipient parkinson’s disease: A longitudinal case study. Brain Cogn. 6(1), 24–29 (2004)CrossRefGoogle Scholar
  22. 22.
    Eichhorn, T.E., Gasser, T., Mai, N., Marquardt, C., Arnold, G., Schwarz, J., Oertel, W.H.: Computational analysis of open loop handwriting movements in parkinson’s disease: A rapid method to detect dopamimetic effects. Mov. Disord. 11(3), 289–297 (1996)CrossRefGoogle Scholar
  23. 23.
    Rosenblum, S., Samuel, M., Zlotnik, S., Erikh, I., Schlesinger, I.: Handwriting as an objective tool for parkinson’s disease diagnosis. J. Neurol. 260(9), 2357–2361 (2013)CrossRefGoogle Scholar
  24. 24.
    Drotár, P., Mekyska, J., Rektorová, I., Masarová, L., Smékal, Z., Faundez-Zanuy, M.: Analysis of in-air movement in handwriting: A novel marker for parkinson’s disease. Comput. Methods Programs Biomed. 117(3), 405–411 (2014)CrossRefGoogle Scholar
  25. 25.
    Pereira, C.R., Pereira, D.R., da Silva, F.A., Hook, C., Weber, S.A.T., Pereira, L.A.M., Papa, J.P.: A step towards the automated diagnosis of parkinson’s disease: Analyzing handwriting movements. In: IEEE 28th International Symposium on Computer-Based Medical Systems, pp. 171–176 (2015)Google Scholar
  26. 26.
    Pasluosta, C.F., Gassner, H., Winkler, J., Klucken, J., Eskofier, B.M.: An emerging era in the management of parkinson’s disease: Wearable technologies and the internet of things. IEEE J. Biomed. Health Inf. 19, 1873–1881 (2015)CrossRefGoogle Scholar
  27. 27.
    Zhao, Y., Heida, T., van Wegen, E.E.H., Bloem, B.R., van Wezel, R.J.A.: E-health support in people with parkinson’s disease with smart glasses: A survey of user requirements and expectations in the netherlands. J. Parkinson’s Dis. 5(2), 369–378 (2015)CrossRefGoogle Scholar
  28. 28.
    Khobragade, N., Graupe, D., Tuninetti, D.: Towards fully automated closed-loop deep brain stimulation in parkinson’s disease patients: A lamstar-based tremor predictor. In: 37th Annual International Conference of the Engineering in Medicine and Biology Society IEEE, p. 2616 (2015)Google Scholar
  29. 29.
    Navarro, G.P., Magariño, I.G., Lorente, P.R.: A kinect-based system for lower limb rehabilitation in parkinson’s disease patients: a pilot study. J. Med. Syst. 39, 1–10 (2015)CrossRefGoogle Scholar
  30. 30.
    Geldenhuys, W.J., Guseman, T.L., Pienaar, I.S., Dluzen, D.E., Young, J.W.: A novel biomechanical analysis of gait changes in the MPTP mouse model of parkinson’s disease. PeerJ PeerJ Comput. Sci. 17, e1175 (2015)Google Scholar
  31. 31.
    Kim, H., Lee, H.J., Lee, W., Kwon, S., Kim, S.K., Jeon, H.S., Park, H., Shin, C.W., Yi, W.J., Jeon, B.S., Park, K.S.: Unconstrained detection of freezing of gait in parkinson’s disease patients using smartphone. In: 37th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) IEEE, pp. 3751–3754 (2015)Google Scholar
  32. 32.
    Papa, J.P., Scheirer, W., Cox, D.D.: Fine-tuning deep belief networks using harmony search. Appl. Soft Comput. 46, 875–885 (2015)CrossRefGoogle Scholar
  33. 33.
    Papa, J.P., Rosa, G.H., Costa, K.A.P., Marana, A.N., Scheirer, W., Cox, D.D.: On the model selection of bernoulli restricted boltzmann machines through harmony search. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO 2015, pp. 1449–1450. ACM, New York, USA (2015)Google Scholar
  34. 34.
    Papa, J.P., Rosa, G.H., Marana, A.N., Scheirer, W., Cox, D.D.: Model selection for discriminative restricted boltzmann machines through meta-heuristic techniques. J. Comput. Sci. 9, 14–18 (2015)CrossRefGoogle Scholar
  35. 35.
    Rosa, G.H., Papa, J.P., Marana, A.N., Scheirer, W., Cox, D.D.: Fine-tuning convolutional neural networks using harmony search. In: Pardo, A., Kittler, J. (eds.) IARP 2015. LNCS, vol. 9423, pp. 683–690. Springer, Heidelberg (2015)Google Scholar
  36. 36.
    Fedorovici, L., Precup, R., Dragan, F., David, R., Purcaru, C.: Embedding gravitational search algorithms in convolutional neural networks for OCR applications. In: 7th IEEE International Symposium on Applied Computational Intelligence and Informatics. SACI 2012, pp. 125–130 (2012)Google Scholar
  37. 37.
    Rere, L.M.R., Fanany, M.I., Arymurthy, A.M.: Metaheuristic algorithms for convolution neural network. Comput. Intell. Neurosci. 2016, 1–13 (2016)CrossRefGoogle Scholar
  38. 38.
    Holzinger, K., Palade, V., Rabadan, R., Holzinger, A.: Darwin or lamarck? future challenges in evolutionary algorithms for knowledge discovery and data mining. Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 35–56. Springer, Heidelberg (2014)Google Scholar
  39. 39.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. arXiv preprint (2014). arXiv:1408.5093
  40. 40.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bull. 1(6), 80–83 (1945)CrossRefGoogle Scholar
  41. 41.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Clayton R. Pereira
    • 1
  • Danillo R. Pereira
    • 2
  • Joao P. Papa
    • 2
    Email author
  • Gustavo H. Rosa
    • 2
  • Xin-She Yang
    • 3
  1. 1.Department of ComputingFederal University of São CarlosSão CarlosBrazil
  2. 2.Department of ComputingSão Paulo State UniversityBauruBrazil
  3. 3.School of Science and TechnologyMiddlesex UniversityLondonUK

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