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Stage Classification of Neuropsychological Tests Based on Decision Fusion

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Advances in Computer Vision and Computational Biology

Abstract

One way to improve classification performance and reliability is the combination of the decisions of multiple classifiers, which is usually known as late fusion. Late fusion has been applied in some biomedical applications, generally, using classic fusion methods, such as mean or majority voting. This work compares the performance of several state-of-the-art fusion methods on a novel biomedical application: automated stage classification of neuropsychological tests using electroencephalographic data. Those tests were made by epileptic patients to evaluate their memory and learning cognitive function with the following stages: stimulus display, retention interval, and subject response. The following late fusion methods were considered: Dempster-Shafer combination; alpha integration; copulas; independent component analysis mixture models; and behavior knowledge space. Late fusion was able to improve the performance of the single classifiers and the most stable results were achieved by alpha integration.

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Acknowledgments

This work was supported by Spanish Administration and European Union grant TEC2017-84743-P.

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Authors and Affiliations

  1. Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, Valencia, Spain

    Gonzalo Safont, Addisson Salazar & Luis Vergara

Authors
  1. Gonzalo Safont
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  2. Addisson Salazar
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  3. Luis Vergara
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Corresponding author

Correspondence to Addisson Salazar .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Georgia, Athens, GA, USA

    Hamid R. Arabnia

  2. School of Computing and Data Sciences, Wentworth Institute of Technology, Boston, MA, USA

    Leonidas Deligiannidis

  3. Graduate School of Information Science & Engineering, University of Electro-Communications, Chofu, Japan

    Hayaru Shouno

  4. Facultad de Informática - CIC PBA, Universidad Nacional de La Plata, La Plata, Argentina

    Fernando G. Tinetti

  5. Department of Computer Science, Southeastern Louisiana University, Hammond, LA, USA

    Quoc-Nam Tran

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Safont, G., Salazar, A., Vergara, L. (2021). Stage Classification of Neuropsychological Tests Based on Decision Fusion. In: Arabnia, H.R., Deligiannidis, L., Shouno, H., Tinetti, F.G., Tran, QN. (eds) Advances in Computer Vision and Computational Biology. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71051-4_65

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  • DOI: https://doi.org/10.1007/978-3-030-71051-4_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71050-7

  • Online ISBN: 978-3-030-71051-4

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