Skip to main content
SpringerLink
Log in
Book cover

European Conference on Advances in Databases and Information Systems

ADBIS 2018: New Trends in Databases and Information Systems pp 153–164Cite as

How Metadata Can Support the Study of Neurological Disorders: An Application to the Alzheimer’s Disease

  • Conference paper
  • First Online:

  • 1186 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 909))

Abstract

This paper deals with the analysis of neurological disorders. In particular we focus on the potentialities of metadata to support the analysis process. We focus on the analysis of the Alzheimer disease and we show how computer-based analysis of metadata associated with clinical observations may help doctors in understanding clinical stages of a patient. We also introduce a general framework than can help in understanding the hypothetical course of the disease by simulating degeneration of the patient by metadata alteration.

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   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

Learn about institutional subscriptions

References

  1. Alviano, M., Faber, W., Leone, N., Perri, S., Pfeifer, G., Terracina, G.: The disjunctive datalog system DLV. In: de Moor, O., Gottlob, G., Furche, T., Sellers, A. (eds.) Datalog 2.0 2010. LNCS, vol. 6702, pp. 282–301. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24206-9_17

    Chapter  Google Scholar 

  2. Chan, K., Fu, A.W.: Efficient time series matching by wavelets. In: Proceedings of the 15th IEEE International Conference on Data Engineering (ICDE 1999), Sydney, Australia, pp. 126–133. IEEE Computer Society Press (1999)

    Google Scholar 

  3. Davatzikos, C., Bhatt, P., Shaw, L.M., Batmanghelich, K.N., Trojanowski, J.O.: Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol. Aging 32(12), 2322-e19–2322-e27 (2011d)

    Article  Google Scholar 

  4. Fernndez, A., Hornero, R., Mayo, A., Poza, J., Gil-Gregorio, P., Ortiz, T.: Meg spectral profile in Alzheimer’s disease and mild cognitive impairment. Clin. Neurophysiol. 117(2), 306–314 (2006)

    Article  Google Scholar 

  5. Hebert, L., Weuve, J., Scherr, P., Evans, D.: Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology 80(19), 1778–1783 (2013)

    Article  Google Scholar 

  6. Hornero, R., Abásolo, D., Escudero, J., Gómez, C.: Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer’s disease. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 367(1887), 317–336 (2009)

    Article  MathSciNet  Google Scholar 

  7. Jeong, J.: EEG dynamics in patients with Alzheimer’s disease. Clin. Neurophysiol. 115(7), 1490–1505 (2004)

    Article  Google Scholar 

  8. Keogh, E.J., Chakrabarti, K., Pazzani, M.J., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. J. Knowl. Inf. Syst. 3(3), 263–286 (2001)

    Article  Google Scholar 

  9. De Meo, P., Quattrone, G., Ursino, D.: Integration of the HL7 standard in a multiagent system to support personalized access to e-health services. IEEE Trans. Knowl. Data Eng. 23(8), 1244–1260 (2011)

    Article  Google Scholar 

  10. Petersen, R.: Mild cognitive impairment as a diagnostic entity. J. Intern. Med. 256(3), 183–194 (2004)

    Article  Google Scholar 

  11. Ramirez, J.: Computer-aided diagnosis of Alzheimer’s type dementia combining support vector machines and discriminant set of features. Inf. Sci. 237, 59–72 (2013)

    Article  Google Scholar 

  12. Stamile, C., Cauteruccio, F., Terracina, G., Ursino, D., Kocevar, G., Sappey-Marinier, D.: A model-guided string-based approach to white matter fiber-bundles extraction. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds.) BIH 2015. LNCS (LNAI), vol. 9250, pp. 135–144. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23344-4_14

    Chapter  Google Scholar 

  13. Yi, B.K., Faloutos, C.: Fast time sequence indexing for arbitrary Lp norms. In: Proceedings of the 26th International Conference on Very Large Databases, Cairo, Egypt, pp. 385–394 (2000)

    Google Scholar 

Download references

Acknowledgement

This work was partially supported by the Italian Ministry for Economic Development (MISE) under the project “Smarter Solutions in the Big Data World”, funded within the call “HORIZON2020” PON I&C 2014–2020.

Author information

Authors and Affiliations

  1. DEMACS, University of Calabria, Rende, Italy

    Francesco Cauteruccio & Giorgio Terracina

Authors
  1. Francesco Cauteruccio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giorgio Terracina
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francesco Cauteruccio .

Editor information

Editors and Affiliations

  1. Eötvös Loránd University, Budapest, Hungary

    András Benczúr

  2. Abt. Informatik, Universität Kiel, Kiel, Germany

    Bernhard Thalheim

  3. Eötvös Loránd University, Budapest, Hungary

    Tomáš Horváth

  4. Politecnico di Torino, Turin, Italy

    Silvia Chiusano

  5. Polytechnic University of Turin, Turin, Italy

    Tania Cerquitelli

  6. Hungarian Academy of Sciences, Budapest, Hungary

    Csaba Sidló

  7. University of Nebraska–Lincoln, Lincoln, NE, USA

    Peter Z. Revesz

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cauteruccio, F., Terracina, G. (2018). How Metadata Can Support the Study of Neurological Disorders: An Application to the Alzheimer’s Disease. In: Benczúr, A., et al. New Trends in Databases and Information Systems. ADBIS 2018. Communications in Computer and Information Science, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-030-00063-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00063-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00062-2

  • Online ISBN: 978-3-030-00063-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation