Visual Sequential Search Test Analysis: An Algorithmic Approach

Giuseppe Alessio D’Inverno, Sara Brunetti, Maria Lucia Sampoli, Dafin Fior Muresanu, Alessandra Rufa and Monica Bianchini
Additional contact information
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
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

Mathematics, 2021, vol. 9, issue 22, 1-13

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.

Keywords: visual sequential search test; episode matching; trail making test; sequence alignment; alignment score (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/22/2952/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/22/2952/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:22:p:2952-:d:682646

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().