A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data

Niccolò Pancino, Caterina Graziani, Veronica Lachi, Maria Lucia Sampoli, Emanuel Ștefǎnescu, Monica Bianchini and Giovanna Maria Dimitri
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Niccolò Pancino: Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze, 50121 Firenze, Italy
Caterina Graziani: Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche, Università degli Studi di Siena, 53100 Siena, Italy
Veronica Lachi: Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche, Università degli Studi di Siena, 53100 Siena, Italy
Maria Lucia Sampoli: Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche, Università degli Studi di Siena, 53100 Siena, Italy
Emanuel Ștefǎnescu: Department of Neurosciences, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
Monica Bianchini: Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche, Università degli Studi di Siena, 53100 Siena, Italy
Giovanna Maria Dimitri: Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche, Università degli Studi di Siena, 53100 Siena, Italy

Mathematics, 2021, vol. 9, issue 24, 1-14

Abstract: Eye-tracking can offer a novel clinical practice and a non-invasive tool to detect neuropathological syndromes. In this paper, we show some analysis on data obtained from the visual sequential search test. Indeed, such a test can be used to evaluate the capacity of looking at objects in a specific order, and its successful execution requires the optimization of the perceptual resources of foveal and extrafoveal vision. The main objective of this work is to detect if some patterns can be found within the data, to discern among people with chronic pain, extrapyramidal patients and healthy controls. We employed statistical tests to evaluate differences among groups, considering three novel indicators: blinking rate, average blinking duration and maximum pupil size variation. Additionally, to divide the three patient groups based on scan-path images—which appear very noisy and all similar to each other—we applied deep learning techniques to embed them into a larger transformed space. We then applied a clustering approach to correctly detect and classify the three cohorts. Preliminary experiments show promising results.

Keywords: eye tracking; Til Making Test; visual sequential search test; neurological diseases; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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