Automatic identification of eye movements using the largest lyapunov exponent

Κορδά, Αλεξάνδρα; Asvestas, Pantelis A.; Matsopoulos, George K.; Ventouras, E.; Smyrnis, Nikolaos

doi:10.1016/j.bspc.2017.11.004

Cited by 14 publications

(9 citation statements)

References 25 publications

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“…It is thus suggested to use broader categories of oculometrics as biomarkers, since they may convey complementary psychophysiological information. Further to the combinatorial features mentioned earlier, an informative class of features are nonlinear features [40,[253][254][255][256][257][258]. The interpretation of nonlinear features, however, may not be as easy as coarse-grained features such as the frequency of saccades.…”

Section: Future Perspectivementioning

confidence: 99%

Aging and Eye Tracking: in the Quest for Objective Biomarkers

Marandi

Gazerani

2019

Future Neurol.

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Recent applications of eye tracking for diagnosis, prognosis and follow-up of therapy in age-related neurological or psychological deficits have been reviewed. The review is focused on active aging, neurodegeneration and cognitive impairments. The potential impacts and current limitations of using characterizing features of eye movements and pupillary responses (oculometrics) as objective biomarkers in the context of aging are discussed. A closer look into the findings, especially with respect to cognitive impairments, suggests that eye tracking is an invaluable technique to study hidden aspects of aging that have not been revealed using any other noninvasive tool. Future research should involve a wider variety of oculometrics, in addition to saccadic metrics and pupillary responses, including nonlinear and combinatorial features as well as blink- and fixation-related metrics to develop biomarkers to trace age-related irregularities associated with cognitive and neural deficits.

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Section: Future Perspectivementioning

confidence: 99%

Aging and Eye Tracking: in the Quest for Objective Biomarkers

Marandi

Gazerani

2019

Future Neurol.

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“…A positive lambda expresses sensitive dependence on initial conditions for a dynamical system. A positive lambda presents the average rate over the whole attractor, at which two nearby trajectories become exponentially separate with time evolution ( 38 ). A practical numerical technique for calculating lambda is the method developed by Rosenstein et al ( 39 ), which works well with small datasets and is robust to changes in the embedding dimension, reconstruction delay, and noise level ( 40 ).…”

Section: Methodsmentioning

confidence: 99%

Chaos analysis of the brain topology in first-episode psychosis and clinical high risk patients

Κορδά

Andreou²,

Avram³

et al. 2022

Front. Psychiatry

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Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis for the identification of brain topology differences in people with psychosis. Structural MRI were acquired from 77 FEP, 73 CHR and 44 healthy controls (HC). Chaos analysis of the gray matter distribution was performed: First, the distances of each voxel from the center of mass in the gray matter image was calculated. Next, the distances multiplied by the voxel intensity were represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts thus how the gray matter topology changes. Between-group differences were identified by (a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and (b) matching the lambda series with the Morlet wavelet, which resulted in statistically significant differences in the scalograms of FEP against CHR and HC. The proposed framework using spatial-series extraction enhances the between-group differences of FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.

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“…A positive lambda expresses sensitive dependence on initial conditions for a dynamical system. A positive lambda presents the average rate over the whole attractor, at which two nearby trajectories become exponentially separate with time evolution [29]. A practical numerical technique for calculating lambda is the method developed by Rosenstein et al [30], which works well with small datasets and is robust to changes in the embedding dimension, reconstruction delay, and noise level [31].…”

Section: Largest Lyapunov Exponentmentioning

confidence: 99%

“…Lambda can be estimated using the matrix X of the reconstructed state space as in [29]. A spatialdependent value of lambda, λ 1 (k), where k the target voxel and T the distance between voxels in the state space, can be estimated as:…”

Section: Largest Lyapunov Exponentmentioning

confidence: 99%

Chaos Analysis of the Brain Topology in Grey Matter Images for the Recognition of Psychosis

Κορδά

Avram

Andreou

et al. 2021

Preprint

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Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis in the identification of people with psychosis. Structural MRI were acquired from 73 CHR, 77 FEP and 44 healthy controls (HC). Chaos analysis of the grey matter distribution was performed: first, the distances of each voxel from the center of mass in the grey matter image was calculated. Next, the distances multiplied by the voxel intensity was represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts how the grey matter topology changes. The classification of a subject’s clinical status was finally predicted by a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and b) matching the lambda series with the Morlet wavelet, which resulted in 100% accuracy in distinguishing between FEP and CHR. The proposed framework using spatial-series extraction enhances the classification decision for FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.

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Automatic identification of eye movements using the largest lyapunov exponent

Cited by 14 publications

References 25 publications

Aging and Eye Tracking: in the Quest for Objective Biomarkers

Aging and Eye Tracking: in the Quest for Objective Biomarkers

Chaos analysis of the brain topology in first-episode psychosis and clinical high risk patients

Chaos Analysis of the Brain Topology in Grey Matter Images for the Recognition of Psychosis

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