Skip to main content
SpringerLink
Log in
Book cover

International Conference on Engineering Applications of Neural Networks

EANN 2017: Engineering Applications of Neural Networks pp 391–403Cite as

Assessment of Parkinson’s Disease Based on Deep Neural Networks

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 744))

Abstract

A novel system based on deep neural networks is presented, that performs analysis of medical imaging data. The aim is to study structural and functional alterations of the human brain in patients with Parkinson’s Disease and to correlate them with epidemiological and clinical data. A new medical database, which is presently under development, is used for training the system and testing its performance. Preliminary experimental results are provided which illustrate the capability of the proposed system to analyze and provide an accurate estimation of the status of the disease.

This work was financed by the State Scholarships Foundation (IKY) through the “Research Projects for Excellence IKY/Siemens” Programme in the framework of the Hellenic Republic – Siemens Settlement Agreement.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Goldman, S.M., Tanner, C.: Etiology of Parkinson’s disease. In: Jankovic, J., Tolosa, E. (eds.) Parkinson’s Disease and Movement Disorders, 3rd edn, pp. 133–158. Williams and Wilkins, Baltimore (1998)

    Google Scholar 

  2. DeMaagd, G., Philip, P.: Parkinson’s disease and its management: Part 1: disease entity risk factors pathophysiology clinical presentation and diagnosis. Pharm. Ther. 40(8), 504 (2015)

    Google Scholar 

  3. Goetz, C., Tilley, B., Shaftman, S., et al.: Movement disorder society-sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov. Disord. 23(15), 2129–2170 (2008)

    Article  Google Scholar 

  4. Hoehn, M., Yahr, M.: Parkinsonism: onset, progression, and mortality. Neurology 50(2), 318–318 (1998)

    Article  Google Scholar 

  5. Goetz, C., Nutt, J., Stebbins, G.: The unified dyskinesia rating scale: presentation and clinimetric profile. Mov. Disord. 23(16), 2398–2403 (2008)

    Article  Google Scholar 

  6. Jenkinson, C., Fitzpatrick, R., Peto, V., Greenhall, R., Hyman, N.: The Parkinson’s disease questionnaire (PDQ-39): development and validation of a Parkinson’s disease summary index score. Age Ageing 26(5), 353–357 (1997)

    Article  Google Scholar 

  7. Giladi, N., Shabtai, H., et al.: Construction of freezing of gait questionnaire for patients with Parkinsonism. Parkinsonism Relat. Disord. 6(3), 165–170 (2000)

    Article  Google Scholar 

  8. Tombaugh, T., McIntyre, N.: The mini-mental state examination: a comprehensive review. J. Am. Geriatr. Soc. 40(9), 922–935 (1992)

    Article  Google Scholar 

  9. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  10. LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of ISCAS, pp. 253–256, Paris, France (2010)

    Google Scholar 

  11. Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional NNs. Adv. Neural. Inf. Process. Syst. 25, 1106–1114 (2012)

    Google Scholar 

  12. He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. https://arxiv.org/abs/1512.03385v1

  13. Ng, H.W., Nguyen, V.D., et al.: Deep learning for emotion recognition on small datasets using transfer learning. In: Proceedings of ACM International Conference on Multimodal Interaction, pp. 443–449 (2015)

    Google Scholar 

  14. Ebrahimi Kahou, S., Michalski, V., et al.: Recurrent neural networks for emotion recognition in video. In: Proceedings of ACM International Conference on Multimodal Interaction, pp. 467–474 (2015)

    Google Scholar 

  15. Wollmer, M., Kaiser, M., et al.: Lstm-modeling of continuous emotions in an audiovisual affect recognition framework. Image Vis. Comput. 31(2), 153–163 (2013)

    Article  Google Scholar 

  16. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

Download references

Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, National Technical University of Athens, Zografou Campus, 15780, Athens, Greece

    Athanasios Tagaris, Dimitrios Kollias & Andreas Stafylopatis

Authors
  1. Athanasios Tagaris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dimitrios Kollias
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andreas Stafylopatis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Athanasios Tagaris .

Editor information

Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Giacomo Boracchi

  2. Democritus University of Thrace, University Campus, Xanthi, Greece

    Lazaros Iliadis

  3. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Univesity of Ioannina, Ioannina, Greece

    Aristidis Likas

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Tagaris, A., Kollias, D., Stafylopatis, A. (2017). Assessment of Parkinson’s Disease Based on Deep Neural Networks. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-65172-9_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65171-2

  • Online ISBN: 978-3-319-65172-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation