Skip to main content
SpringerLink
Log in

IGrC: Cognitive and Motor Changes During Symptoms Development in Parkinson’s Disease Patients

  • Conference paper
  • First Online:
Book cover Intelligent Information and Database Systems (ACIIDS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12034))

Included in the following conference series:

Abstract

Cognitive symptoms are characteristic for neurodegenerative disease: there are dominating in the Alzheimer’s, but secondary in Parkinson’s disease (PD). However, in PD motor symptoms (MS) are dominating and their characteristic helps neurologist to recognize the disease. There are a large number of data mining publications that analyzed MS in PD. Present study is related to the question if development of cognitive symptoms is related to motor symptoms or if they are two independent processes? We have responded to this problem with help of IGrC (intelligent granular computing) approach. We have put together eye movement, neurological and neuropsychological tests. Our study was dedicated to 47 Parkinson’s disease patients in two sessions: S#1 - without medications (MedOFF) and S#2 after taking medications (MedON). There were two groups of patients: Gr1 (23 patients) less advanced and Gr2 more advanced PD. We have measured Gr1 in three visits every 6 months: Gr1VIS1, Gr1VIS2, Gr1VIS3. Gr2 (24 patients) has only one visit (no visit number). With rough set theory (RST) that belongs to IGrC we have found from Gr2 three different sets of rules: a) general rules (GRUL) determined by all attributes; b) motor related rules (MRUL) – motor attributes; c) cognitive rules (CRUL) determined by cognitive attributes. By applying these different sets of rules to different Gr1 visits we have found different set of symptoms developments. With GRUL we have found for Gr1VIS1 accuracy = 0.682, for Gr1VIS2 acc. = 0.857, for Gr1VIS3 acc. = 0.875. With MRUL we have found for Gr1VIS1 acc. = 0.80, for Gr1VIS2 acc. = 0.933, for Gr1VIS3 acc. = 1.0. With CRUL we have found for Gr1VIS1 acc. = 0.50, for Gr1VIS2 acc. = 0.60, for Gr1VIS3 acc. = 0.636. Cognitive changes are independent from the motor symptoms development.

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

Access this chapter

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

References

  1. Przybyszewski, A.W., Kon, M., Szlufik, S., Szymanski, A., Koziorowski, D.M.: Multimodal learning and intelligent prediction of symptom development in individual Parkinson’s patients. Sensors 16(9), 1498 (2016)

    Article  Google Scholar 

  2. Przybyszewski, A.W.: Fuzzy RST and RST rules can predict effects of different therapies in Parkinson’s disease patients. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G.A., Raś, Z.W. (eds.) ISMIS 2018. LNCS (LNAI), vol. 11177, pp. 409–416. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01851-1_39

    Chapter  Google Scholar 

  3. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Springer, Dordrecht (1991). https://doi.org/10.1007/978-94-011-3534-4

    Book  MATH  Google Scholar 

  4. Jankowski, A.: Interactive Granular Computations in Networks and Systems Engineering: A Practical Perspective. LNNS, vol. 17. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57627-5

    Book  Google Scholar 

  5. Bazan, J., Nguyen, S.H., et al.: Desion rules synthesis for object classification. In: Orłowska, E. (ed.) Incomplete Information: Rough Set Analysis, Physica – Verlag, Heidelberg, pp. 23–57 (1998)

    Google Scholar 

  6. Przybyszewski, A.W.: Applying data mining and machine learning algorithms to predict symptom development in Parkinson’s disease. Ann. Acad. Med. Siles. 68(5), 332–349 (2014)

    Google Scholar 

  7. Bazan, J., Nguyen, H.S., Nguyen, S.H., et al.: Rough set algorithms in classification problem. In: Polkowski, L., Tsumoto, S., Lin, T. (eds.) Rough Set Methods and Applications, pp. 49–88. Physica-Verlag, Heidelberg (2000). https://doi.org/10.1007/978-3-7908-1840-6_3

    Chapter  Google Scholar 

  8. Grzymała-Busse, J.: A New Version of the Rule Induction System LERS. Fundamenta Informaticae 31(1), 27–39 (1997)

    Article  MathSciNet  Google Scholar 

  9. Bazan, J.G., Szczuka, M.: The rough set exploration system. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37–56. Springer, Heidelberg (2005). https://doi.org/10.1007/11427834_2

    Chapter  MATH  Google Scholar 

  10. Przybyszewski, A.W.: The neurophysiological bases of cognitive computation using rough set theory. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 287–317. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89876-4_16

    Chapter  Google Scholar 

  11. Bazan, J.G., Szczuka, M.: RSES and RSESlib - a collection of tools for rough set computations. In: Ziarko, W., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 106–113. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45554-X_12

    Chapter  MATH  Google Scholar 

  12. Przybyszewski, A.W., Nowacki, J.P., Drabik, A., Szlufik, S., Habela, P., Koziorowski, D.M.: Granular computing (GC) demonstrates interactions between depression and symptoms development in Parkinson’s disease patients. In: Nguyen, N.T., Gaol, F.L., Hong, T.-P., Trawiński, B. (eds.) ACIIDS 2019. LNCS (LNAI), vol. 11432, pp. 591–601. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14802-7_51

    Chapter  Google Scholar 

  13. Przybyszewski, A.W.: Parkinson’s Disease Development Prediction by C-Granule Computing. In: Nguyen, N.T., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds.) ICCCI 2019. LNCS (LNAI), vol. 11683, pp. 296–306. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28377-3_24

    Chapter  Google Scholar 

  14. Khoury, N., Attal, F., Amirat, Y., Oukhellou, L., Mohammed, S.: Data-driven based approach to aid Parkinson’s disease diagnosis. Sensors (Basel) 19(2), 242 (2019). https://doi.org/10.3390/s19020242

    Article  Google Scholar 

  15. Yadav, G., Kumar, Y., Sahoo, G.: Predication of Parkinson’s disease using data mining methods: a comparative analysis of tree, statistical, and support vector machine classifiers. Indian J. Med. Sci. 65(6), 231–242 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Polish-Japanese Academy of Information Technology, 02-008, Warsaw, Poland

    Andrzej W. Przybyszewski, Jerzy Paweł Nowacki & Aldona Drabik

  2. Department of Neurology, Faculty of Health Science, Medical University of Warsaw, Warsaw, Poland

    Stanislaw Szlufik & Dariusz M. Koziorowski

Authors
  1. Andrzej W. Przybyszewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jerzy Paweł Nowacki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aldona Drabik
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stanislaw Szlufik
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dariusz M. Koziorowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrzej W. Przybyszewski .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Kietikul Jearanaitanakij

  3. Faculty of Computer Science and Information, University Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  4. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Przybyszewski, A.W., Nowacki, J.P., Drabik, A., Szlufik, S., Koziorowski, D.M. (2020). IGrC: Cognitive and Motor Changes During Symptoms Development in Parkinson’s Disease Patients. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12034. Springer, Cham. https://doi.org/10.1007/978-3-030-42058-1_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-42058-1_46

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-42057-4

  • Online ISBN: 978-3-030-42058-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation