An optimized Kernel Extreme Learning Machine for the classification of the autism spectrum disorder by using gaze tracking images

Gaspar, Angel; Oliva, Diego; Hinojosa, Salvador; Aranguren, Itzel; Zaldívar, Daniel

doi:10.1016/j.asoc.2022.108654

Cited by 27 publications

(11 citation statements)

References 41 publications

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“…Previous studies have shown the feasibility of using classification models for the early detection of ASD by utilizing gaze features such as entropy, areas of interest, and fixation analysis (Gaspar et al, 2022; Kang et al, 2020; Mazumdar et al, 2021; Startsev & Dorr, 2019; Zhao et al, 2021; Zhao, Wei, et al, 2022). We have obtained preliminary results for predicting ASD in our sample using logistic regression along with the oculomotor parameters developed in this study.…”

Section: Discussionmentioning

confidence: 99%

Oculomotor randomness is higher in autistic children and increases with the severity of symptoms

Ziv,

Avni,

Dinstein

et al. 2024

Autism Research

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A variety of studies have suggested that at least some children with autism spectrum disorder (ASD) view the world differently. Differences in gaze patterns as measured by eye tracking have been demonstrated during visual exploration of images and natural viewing of movies with social content. Here we analyzed the temporal randomness of saccades and blinks during natural viewing of movies, inspired by a recent measure of “randomness” applied to micro‐movements of the hand and head in ASD (Torres et al., 2013; Torres & Denisova, 2016). We analyzed a large eye‐tracking dataset of 189 ASD and 41 typically developing (TD) children (1–11 years old) who watched three movie clips with social content, each repeated twice. We found that oculomotor measures of randomness, obtained from gamma parameters of inter‐saccade intervals (ISI) and blink duration distributions, were significantly higher in the ASD group compared with the TD group and were correlated with the ADOS comparison score, reflecting increased “randomness” in more severe cases. Moreover, these measures of randomness decreased with age, as well as with higher cognitive scores in both groups and were consistent across repeated viewing of each movie clip. Highly “random” eye movements in ASD children could be associated with high “neural variability” or noise, poor sensory‐motor control, or weak engagement with the movies. These findings could contribute to the future development of oculomotor biomarkers as part of an integrative diagnostic tool for ASD.

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Section: Discussionmentioning

confidence: 99%

Oculomotor randomness is higher in autistic children and increases with the severity of symptoms

Ziv,

Avni,

Dinstein

et al. 2024

Autism Research

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show abstract

“…e proposed model is an integer quadratic problem that can be solved through the bifurcation and delimitation algorithm to obtain the optimal solution [20][21][22][23]. Both certain and robust models have been solved using GAMS software and Windows 10 using a laptop that is equipped with an Intel Core i7 processor 8 GB of RAM.…”

Section: Test Problem Designmentioning

confidence: 99%

“…Moreover, in cases where explicit data were not available, ten experiments were performed in which the parameters were obtained by multiplying the numbers obtained from the previous data in an interval (1 + ε) where ε was obtained from a uniform distribution [−0.1, 0.1]. For more information, see [3,5,[21][22][23]. e average speed of a car is 30 km/h.…”

Section: Comparing the Objective Functionsmentioning

confidence: 99%

The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances

Asgharizadeh

Kadivar

Noroozi

et al. 2022

Computational Intelligence and Neuroscience

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In the present study, the optimization of medical services considering the role of intelligent traffic management is of concern. In this regard, a two-objective mathematical model of a medical emergency system is assessed in order to determine the location of emergency stations and determine the required number of ambulances to be allocated to the station. The objective functions are the maximization of covering the emergency demands and minimization of total costs. Moreover, the use of an intelligent traffic management system to speed up the ambulance is addressed. In this regard, the proposed two-objective mathematical model has been formulated, and a robust counterpart formulation under uncertainty is applied. In the proposed method, the values of the objective function increase as the problem becomes wider and, with a slight difference in large dimensions, converge in terms of the solution. The numerical results indicate that, as the problem's complexity increases, the robust optimization method is still effective because, with the increasing complexity of the problem, it can still solve large-scale problems in a reasonable time. Moreover, the difference between the value of the objective function in the proposed method and the presence of uncertainty parameters is very small and, in large dimensions, is quite logical and negligible. The sensitivity analysis shows that, with increasing demand, both the number of ambulances required and the amount of objective function have increased significantly.

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“…The images illustrated the dynamics of eye movements in visualisations of the eye-tracking scanpaths. The dataset was used in numerous publications such as (Ahmed, and Jadhav, 2020), (Akter et al, 2021), (Gaspar et al, 2022), and (Elbattah et al, 2022). It is hoped that the present dataset will also deem useful to develop further applications for the ASD diagnosis.…”

Section: Eye-tracking Research In Autismmentioning

confidence: 99%

Eye-Tracking Dataset to Support the Research on Autism Spectrum Disorder

Cilia

Carette

Elbattah

et al. 2022

Preprint

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The availability of data is a key enabler for researchers across different disciplines. However, domains, such as healthcare, are still fundamentally challenged by the paucity and imbalance of datasets. Health data could be inaccessible due to a variety of hurdles such as privacy concerns, or lack of sharing incentives. In this regard, this study aims to publish an eye-tracking dataset developed for the purpose of autism diagnosis. Eye-tracking methods are used intensively in that context, whereas abnormalities of the eye gaze are largely recognised as the hallmark of autism. As such, it is believed that the dataset can allow for developing useful applications or discovering interesting insights. As well, Machine Learning is a potential application for developing diagnostic models that can help detect autism at an early stage of development.

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An optimized Kernel Extreme Learning Machine for the classification of the autism spectrum disorder by using gaze tracking images

Cited by 27 publications

References 41 publications

Oculomotor randomness is higher in autistic children and increases with the severity of symptoms

Oculomotor randomness is higher in autistic children and increases with the severity of symptoms

The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances

Eye-Tracking Dataset to Support the Research on Autism Spectrum Disorder

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