Ocular Motility Recording and Nystagmus

Dell’Osso, Louis F.; Abel, Larry A

doi:10.1002/0471732877.emd188

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…Presently, these tasks are more easily achievable in a laboratory environment. We are therefore greatly encouraged by the improved availability of clinic-friendly 2- and 3-dimensional eye movement recording systems 62 , 63 . Their high resolution, greater linearity, improved sampling rates and noise reduction properties are particularly important for examining nystagmus waveforms, which can vary from cycle to cycle.…”

Section: Discussionmentioning

confidence: 99%

Analysing nystagmus waveforms: a computational framework

Abadi

Akman

Arblaster

et al. 2021

Sci Rep

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We present a new computational approach to analyse nystagmus waveforms. Our framework is designed to fully characterise the state of the nystagmus, aid clinical diagnosis and to quantify the dynamical changes in the oscillations over time. Both linear and nonlinear analyses of time series were used to determine the regularity and complexity of a specific homogenous phenotype of nystagmus. Two-dimensional binocular eye movement recordings were carried out on 5 adult subjects who exhibited a unilateral, uniplanar, vertical nystagmus secondary to a monocular late-onset severe visual loss in the oscillating eye (the Heimann-Bielschowsky Phenomenon). The non-affected eye held a central gaze in both horizontal and vertical planes (± 10 min. of arc). All affected eyes exhibited vertical oscillations, with mean amplitudes and frequencies ranging from 2.0°–4.0° to 0.25–1.5 Hz, respectively. Unstable periodic orbit analysis revealed only 1 subject exhibited a periodic oscillation. The remaining subjects were found to display quasiperiodic (n = 1) and nonperiodic (n = 3) oscillations. Phase space reconstruction allowed attractor identification and the computation of a time series complexity measure—the permutation entropy. The entropy measure was found to be able to distinguish between a periodic oscillation associated with a limit cycle attractor, a quasiperiodic oscillation associated with a torus attractor and nonperiodic oscillations associated with higher-dimensional attractors. Importantly, the permutation entropy was able to rank the oscillations, thereby providing an objective index of nystagmus complexity (range 0.15–0.21) that could not be obtained via unstable periodic orbit analysis or attractor identification alone. These results suggest that our framework provides a comprehensive methodology for characterising nystagmus, aiding differential diagnosis and also permitting investigation of the waveforms over time, thereby facilitating the quantification of future therapeutic managements. In addition, permutation entropy could provide an additional tool for future oculomotor modelling.

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Section: Discussionmentioning

confidence: 99%

Analysing nystagmus waveforms: a computational framework

Abadi

Akman

Arblaster

et al. 2021

Sci Rep

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“…At present, accurately calibrating an eye tracker in the presence of nystagmus is a time-consuming process, requiring an expert observer to manually select the foveation periods from an eye trace and calibrate post hoc (Dell’Osso & Abel, 2006) . Alternatively, the operator may opt to ignore foveations altogether and simply use the average eye position from the entire eye trace for the duration during which the calibration target was presented.…”

mentioning

confidence: 99%

An automated segmentation approach to calibrating infantile nystagmus waveforms

et al. 2019

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Infantile nystagmus (IN) describes a regular, repetitive movement of the eyes. A characteristic feature of each cycle of the IN eye movement waveform is a period in which the eyes are moving at minimal velocity. This so-called “foveation” period has long been considered the basis for the best vision in individuals with IN. In recent years, the technology for measuring eye movements has improved considerably, but there remains the challenge of calibrating the direction of gaze in tracking systems when the eyes are continuously moving. Identifying portions of the nystagmus waveform suitable for calibration typically involves time-consuming manual selection of the foveation periods from the eye trace. Without an accurate calibration, the exact parameters of the waveform cannot be determined. In this study, we present an automated method for segmenting IN waveforms with the purpose of determining the foveation positions to be used for calibration of an eye tracker. On average, the “point of regard” was found to be within 0.21° of that determined by hand-marking by an expert observer. This method enables rapid clinical quantification of waveforms and the possibility of gaze-contingent research paradigms being performed with this patient group.

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Ocular Motility Recording and Nystagmus

Cited by 2 publications

References 27 publications

Analysing nystagmus waveforms: a computational framework

Analysing nystagmus waveforms: a computational framework

An automated segmentation approach to calibrating infantile nystagmus waveforms

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