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Visual Sequential Search Test Analysis: An Algorithmic Approach

Author

Listed:
  • Giuseppe Alessio D’Inverno

    (Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy)

  • Sara Brunetti

    (Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy)

  • Maria Lucia Sampoli

    (Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy)

  • Dafin Fior Muresanu

    (Department of Neurosciences, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400023 Cluj-Napoca, Romania
    RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania)

  • Alessandra Rufa

    (Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy)

  • Monica Bianchini

    (Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy)

Abstract

In this work we present an algorithmic approach to the analysis of the Visual Sequential Search Test (VSST) based on the episode matching method. The data set included two groups of patients, one with Parkinson’s disease, and another with chronic pain syndrome, along with a control group. The VSST is an eye-tracking modified version of the Trail Making Test (TMT) which evaluates high order cognitive functions. The episode matching method is traditionally used in bioinformatics applications. Here it is used in a different context which helps us to assign a score to a set of patients, under a specific VSST task to perform. Experimental results provide statistical evidence of the different behaviour among different classes of patients, according to different pathologies.

Suggested Citation

  • Giuseppe Alessio D’Inverno & Sara Brunetti & Maria Lucia Sampoli & Dafin Fior Muresanu & Alessandra Rufa & Monica Bianchini, 2021. "Visual Sequential Search Test Analysis: An Algorithmic Approach," Mathematics, MDPI, vol. 9(22), pages 1-13, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2952-:d:682646
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