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.
D'Inverno, G.A., Brunetti, S., Sampoli, M.L., Fior Muresanu, D., Rufa, A., Bianchini, M. (2021). Visual Sequential Search Test Analysis: An Algorithmic Approach. MATHEMATICS, 9(22) [10.3390/math9222952].
Visual Sequential Search Test Analysis: An Algorithmic Approach
Giuseppe Alessio D’Inverno;Sara Brunetti;Maria Lucia Sampoli;Alessandra Rufa;Monica Bianchini
2021-01-01
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.File | Dimensione | Formato | |
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https://hdl.handle.net/11365/1168613