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Meo MM, Sánchez Pavón I, Duarte CD, Del Punta JA, Martín Herranz R, Gasaneo G. Multifractal characterization of nystagmus eye movements. CHAOS (WOODBURY, N.Y.) 2024; 34:043137. [PMID: 38619247 DOI: 10.1063/5.0194768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 03/27/2024] [Indexed: 04/16/2024]
Abstract
In this work, we investigate the multifractal properties of eye movement dynamics of children with infantile nystagmus, particularly the fluctuations of its velocity. The eye movements of three children and one adult with infantile nystagmus were evaluated in a simple task in comparison with 28 children with no ocular pathologies. Four indices emerge from the analysis: the classical Hurst exponent, the singularity strength corresponding to the maximum of the singularity spectrum, the asymmetry of the singularity spectrum, and the multifractal strength, each of which characterizes a particular aspect of eye movement dynamics. Our findings indicate that, when compared to children with no ocular pathologies, patients with infantile nystagmus present lower values of all indices. Except for the multifractal strength, the difference in the remaining indices is statistically significant. To test whether the characterization of patients with infantile nystagmus in terms of multifractality indices allows them to be distinguished from children without ocular pathologies, we performed an unsupervised clustering analysis and classified the subjects using supervised clustering techniques. The results indicate that these indices do, indeed, distinctively characterize the eye movements of patients with infantile nystagmus.
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Affiliation(s)
- M M Meo
- Instituto de Física del Sur, Departamento de Física, Universidad Nacional del Sur (UNS)-CONICET, 8000 Bahía Blanca, Argentina
| | - I Sánchez Pavón
- Optometry Research Group, IOBA Eye Institute, School of Optometry, University of Valladolid, 47011 Valladolid, Spain
- Departamento de Física Teórica Atómica y Óptica, Universidad de Valladolid, 47011 Valladolid, Spain
| | - C D Duarte
- Instituto de Física del Sur, Departamento de Física, Universidad Nacional del Sur (UNS)-CONICET, 8000 Bahía Blanca, Argentina
| | - J A Del Punta
- Instituto de Física del Sur, Departamento de Física, Universidad Nacional del Sur (UNS)-CONICET and Departamento de Matemática, Universidad Nacional del Sur (UNS), 8000 Bahía Blanca, Argentina
| | - R Martín Herranz
- Optometry Research Group, IOBA Eye Institute, School of Optometry, University of Valladolid, 47011 Valladolid, Spain
- Departamento de Física Teórica Atómica y Óptica, Universidad de Valladolid, 47011 Valladolid, Spain
| | - G Gasaneo
- Instituto de Física del Sur, Departamento de Física, Universidad Nacional del Sur (UNS)-CONICET and Centro Integral de Neuricencias Aplicadas, 8000 Bahía Blanca, Argentina
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Suman AA, Russo C, Carrigan A, Nalepka P, Liquet-Weiland B, Newport RA, Kumari P, Di Ieva A. Spatial and time domain analysis of eye-tracking data during screening of brain magnetic resonance images. PLoS One 2021; 16:e0260717. [PMID: 34855867 PMCID: PMC8639086 DOI: 10.1371/journal.pone.0260717] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 11/15/2021] [Indexed: 12/01/2022] Open
Abstract
INTRODUCTION Eye-tracking research has been widely used in radiology applications. Prior studies exclusively analysed either temporal or spatial eye-tracking features, both of which alone do not completely characterise the spatiotemporal dynamics of radiologists' gaze features. PURPOSE Our research aims to quantify human visual search dynamics in both domains during brain stimuli screening to explore the relationship between reader characteristics and stimuli complexity. The methodology can be used to discover strategies to aid trainee radiologists in identifying pathology, and to select regions of interest for machine vision applications. METHOD The study was performed using eye-tracking data 5 seconds in duration from 57 readers (15 Brain-experts, 11 Other-experts, 5 Registrars and 26 Naïves) for 40 neuroradiological images as stimuli (i.e., 20 normal and 20 pathological brain MRIs). The visual scanning patterns were analysed by calculating the fractal dimension (FD) and Hurst exponent (HE) using re-scaled range (R/S) and detrended fluctuation analysis (DFA) methods. The FD was used to measure the spatial geometrical complexity of the gaze patterns, and the HE analysis was used to measure participants' focusing skill. The focusing skill is referred to persistence/anti-persistence of the participants' gaze on the stimulus over time. Pathological and normal stimuli were analysed separately both at the "First Second" and full "Five Seconds" viewing duration. RESULTS All experts were more focused and a had higher visual search complexity compared to Registrars and Naïves. This was seen in both the pathological and normal stimuli in the first and five second analyses. The Brain-experts subgroup was shown to achieve better focusing skill than Other-experts due to their domain specific expertise. Indeed, the FDs found when viewing pathological stimuli were higher than those in normal ones. Viewing normal stimuli resulted in an increase of FD found in five second data, unlike pathological stimuli, which did not change. In contrast to the FDs, the scanpath HEs of pathological and normal stimuli were similar. However, participants' gaze was more focused for "Five Seconds" than "First Second" data. CONCLUSIONS The HE analysis of the scanpaths belonging to all experts showed that they have greater focus than Registrars and Naïves. This may be related to their higher visual search complexity than non-experts due to their training and expertise.
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Affiliation(s)
- Abdulla Al Suman
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
| | - Carlo Russo
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
| | - Ann Carrigan
- School of Psychological Sciences, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
- Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, Australia
| | - Patrick Nalepka
- School of Psychological Sciences, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
- Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, Australia
| | - Benoit Liquet-Weiland
- Department of Mathematics and Statistics, Faculty of Science and Engineering, Macquarie University, Sydney, Australia
| | - Robert Ahadizad Newport
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
| | - Poonam Kumari
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health, and Human Sciences, Macquarie University, Sydney, Australia
- Centre for Elite Performance, Expertise and Training, Macquarie University, Sydney, Australia
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