Analysing nystagmus waveforms: a computational framework

Abstract

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.

Eye tracking in optometry: A systematic review

“…Some studies reported on the use of head‑mounted trackers. Such devices are useful for taking measurements in different environments and even during persons’ daily activities or real-life situations ( 1 ; 25 ; 28 ; 39 ; 40 ).…”

“…Some studies reported on the use of head-mounted trackers. Such devices are useful for taking measurements in different environments and even during persons' daily activities or real-life situations (Abadi et al, 2021;Chaudhary et al, 2020;Chow-Wing-Bom et al, 2020;Freedman et al, 2019;Fujimoto et al, 2022).…”

Electrophysiological analysis of ENG signals in patients with Covid-19

“…Out of the traditional clinic setting, A linear analysis systems have been used to determine the periodic nature of the waveforms by using Fourier transform, Short Fourier transform, and wavelet analysis ( Abadi et al, 2021 , Rosengren et al, 2020 ). More recently, nonlinear dynamical systems theory has provided additional important computational advances.…”

“…The application of nonlinear analysis is the modelling of the underlying neural behavior responsible for the oscillations. The entropy measure was found to be able to distinguish between a periodic oscillation associated with a limit cycle attractor, a quasi-periodic oscillation associated with a torus attractor, and nonperiodic oscillations associated with higher dimensional attractors ( Abadi et al, 2021 ).…”