An Analysis of Eye-Tracking Features and Modelling Methods for Free-Viewed Standard Stimulus: Application for Schizophrenia Detection

Kačur, Juraj; Polec, Jaroslav; Smolejova, Eva; Heretik, Anton

doi:10.1109/jbhi.2020.3002097

Cited by 18 publications

(10 citation statements)

References 14 publications

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“…Fixation duration has been used to gain insights into cognitive control capacity; for example, what we remember, 123, 124 how we perform mental computations, 125 read, 126 solve problems, 89, 127–133 compare specific stimuli and make decisions based on integrated pieces of information 134 . Eye‐tracking technology also provides knowledge about regions of interest, and therefore answers essential questions of when and how information is being captured and processed during vision‐oriented tests 92, 135–140 . Of great interest is the time spent on an image; this gaze metric has been widely covered in the scientific literature and is associated with the visual expression of preference 141–144 …”

Section: (Eye‐)tracking the Cognitionmentioning

confidence: 99%

Recent updates of eye movement abnormalities in patients with schizophrenia: A scoping review

Wolf

Ueda

Hirano

2021

Psychiatry Clin Neurosci

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Aim Although eye‐tracking technology expands beyond capturing eye data just for the sole purpose of ensuring participants maintain their gaze at the presented fixation cross, gaze technology remains of less importance in clinical research. Recently, impairments in visual information encoding processes indexed by novel gaze metrics have been frequently reported in patients with schizophrenia. This work undertakes a scoping review of research on saccadic dysfunctions and exploratory eye movement deficits among patients with schizophrenia. It gathers promising pieces of evidence of eye movement abnormalities in attention‐demanding tasks on the schizophrenia spectrum that have mounted in recent years and their outcomes as potential biological markers. Methods The protocol was drafted based on PRISMA for scoping review guidelines. Electronic databases were systematically searched to identify articles published between 2010 and 2020 that examined visual processing in patients with schizophrenia and reported eye movement characteristics as potential biomarkers for this mental illness. Results The use of modern eye‐tracking instrumentation has been reported by numerous neuroscientific studies to successfully and non‐invasively improve the detection of visual information processing impairments among the screened population at risk of and identified with schizophrenia. Conclusions Eye‐tracking technology has the potential to contribute to the process of early intervention and more apparent separation of the diagnostic entities, being put together by the syndrome‐based approach to the diagnosis of schizophrenia. However, context‐processing paradigms should be conducted and reported in equally accessible publications to build comprehensive models.

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Section: (Eye‐)tracking the Cognitionmentioning

confidence: 99%

Recent updates of eye movement abnormalities in patients with schizophrenia: A scoping review

Wolf

Ueda

Hirano

2021

Psychiatry Clin Neurosci

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“…The number of splits was set to 1 for the test data and 8:2 for the training and validation data. The sample size for each learning was 630 or 651, which is sufficient compared to the previous study ( 24 ). We used PyTorch ( 40 ) for implementation.…”

Section: Modeling and Evaluationmentioning

confidence: 99%

“…The number of splits was set to 1 for the test data and 8:2 for the training and validation data. Since the sample size for each learning was 630 or 651, which is sufficient compared to a previous study ( 24 ), we did not consider it necessary to use such traditional approaches as a support vector machine. For reference, the loss during training of the CNN in the Sz and control group classifications is shown in Figure 6 .…”

Section: Modeling and Evaluationmentioning

confidence: 99%

“…Kacur et al used eye movement during Rorschach tests (for mental discrimination) and analyzed them using various machine learning methods. Their results showed that the best classification results for Sz and control were obtained by constructing a convolutional neural network (CNN) model with a heat map showing eye-movement pauses ( 24 ).…”

Section: Introductionmentioning

confidence: 99%

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Eye-movement analysis on facial expression for identifying children and adults with neurodevelopmental disorders

Iwauchi

Tanaka²,

Okazaki³

et al. 2023

Front. Digit. Health

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Experienced psychiatrists identify people with autism spectrum disorder (ASD) and schizophrenia (Sz) through interviews based on diagnostic criteria, their responses, and various neuropsychological tests. To improve the clinical diagnosis of neurodevelopmental disorders such as ASD and Sz, the discovery of disorder-specific biomarkers and behavioral indicators with sufficient sensitivity is important. In recent years, studies have been conducted using machine learning to make more accurate predictions. Among various indicators, eye movement, which can be easily obtained, has attracted much attention and various studies have been conducted for ASD and Sz. Eye movement specificity during facial expression recognition has been studied extensively in the past, but modeling taking into account differences in specificity among facial expressions has not been conducted. In this paper, we propose a method to detect ASD or Sz from eye movement during the Facial Emotion Identification Test (FEIT) while considering differences in eye movement due to the facial expressions presented. We also confirm that weighting using the differences improves classification accuracy. Our data set sample consisted of 15 adults with ASD and Sz, 16 controls, and 15 children with ASD and 17 controls. Random forest was used to weight each test and classify the participants as control, ASD, or Sz. The most successful approach used heat maps and convolutional neural networks (CNN) for eye retention. This method classified Sz in adults with 64.5% accuracy, ASD in adults with up to 71.0% accuracy, and ASD in children with 66.7% accuracy. Classifying of ASD result was significantly different (p<.05) by the binomial test with chance rate. The results show a 10% and 16.7% improvement in accuracy, respectively, compared to a model that does not take facial expressions into account. In ASD, this indicates that modeling is effective, which weights the output of each image.

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“…Eye movement abnormalities have been identified as an “endophenotype” in schizophrenia ( Kacur et al, 2020 ). Schizophrenia patients manifested abnormal performances in multiple eye movement tasks, comprising fixation stability, free-viewing, smooth pursuit, and saccadic tests ( Benson et al, 2012 ; Morita et al, 2017 ; Wolf et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

The impact of COVID-19 pandemic on individuals at clinical high-risk for psychosis: Evidence from eye-tracking measures

Zhang

Guo

et al. 2022

Progress in Neuro-Psychopharmacology and Biological Psychiatry

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An Analysis of Eye-Tracking Features and Modelling Methods for Free-Viewed Standard Stimulus: Application for Schizophrenia Detection

Cited by 18 publications

References 14 publications

Recent updates of eye movement abnormalities in patients with schizophrenia: A scoping review

Recent updates of eye movement abnormalities in patients with schizophrenia: A scoping review

Eye-movement analysis on facial expression for identifying children and adults with neurodevelopmental disorders

The impact of COVID-19 pandemic on individuals at clinical high-risk for psychosis: Evidence from eye-tracking measures

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