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Volume 9
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10.3390/math9243159
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Article

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

by
Niccolò Pancino
1,2,†,
Caterina Graziani
2,†,
Veronica Lachi
2,†,
Maria Lucia Sampoli
2,
Emanuel Ștefǎnescu
3,4,
Monica Bianchini
2 and
Giovanna Maria Dimitri
2,5,*
1
Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze, 50121 Firenze, Italy
2
Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche, Università degli Studi di Siena, 53100 Siena, Italy
3
Department of Neurosciences, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
4
RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania
5
Dipartimento di Informatica, Università di Pisa, 56127 Pisa, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Mathematics 2021, 9(24), 3159; https://doi.org/10.3390/math9243159
Submission received: 18 October 2021 / Revised: 29 November 2021 / Accepted: 6 December 2021 / Published: 8 December 2021
(This article belongs to the Special Issue Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications)
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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 eye tracking; Til Making Test; visual sequential search test; neurological diseases; deep learning

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MDPI and ACS Style

Pancino, N.; Graziani, C.; Lachi, V.; Sampoli, M.L.; Ștefǎnescu, E.; Bianchini, M.; Dimitri, G.M. A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data. Mathematics 2021, 9, 3159. https://doi.org/10.3390/math9243159

AMA Style

Pancino N, Graziani C, Lachi V, Sampoli ML, Ștefǎnescu E, Bianchini M, Dimitri GM. A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data. Mathematics. 2021; 9(24):3159. https://doi.org/10.3390/math9243159

Chicago/Turabian Style

Pancino, Niccolò, Caterina Graziani, Veronica Lachi, Maria Lucia Sampoli, Emanuel Ștefǎnescu, Monica Bianchini, and Giovanna Maria Dimitri. 2021. "A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data" Mathematics 9, no. 24: 3159. https://doi.org/10.3390/math9243159

APA Style

Pancino, N., Graziani, C., Lachi, V., Sampoli, M. L., Ștefǎnescu, E., Bianchini, M., & Dimitri, G. M. (2021). A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data. Mathematics, 9(24), 3159. https://doi.org/10.3390/math9243159

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