“…strange or chaotic attractors) (Fig. 2 c) 22 , 25 , 27 , 34 , 49 . Figure 2 A comparison between stable (first row ( a )) and unstable fixation (second row ( b ) and third row ( c )).…”
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.
“…strange or chaotic attractors) (Fig. 2 c) 22 , 25 , 27 , 34 , 49 . Figure 2 A comparison between stable (first row ( a )) and unstable fixation (second row ( b ) and third row ( c )).…”
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.
“…During visual fixation, the eyes produce small involuntary movements, called fixational eye movements (FEMs), which are conventionally separated into saccadic-like jumps (more generally referred to as saccadic intrusions, fixational saccades, or microsaccades), drift, and tremor (Steinman, Haddad, Skavenski, & Wyman, 1973;Abadi, Clement, & Gowen, 2003;Collewijn & Kowler, 2008). During the last decades FEM research has gained a renewed interest due to the possible involvement in perception and attention (Engbert, 2006;Hafed, Chen, & Tian, 2015) and to neurophysiological evidence showing that neural activity is sensitive to different types of FEMs (Martinez-Conde, Macknik, & Hubel, 2000;Kagan, Gur, & Snodderly, 2008).…”
Micromovements of the eye during visual fixations provide clues about how our visual system acquires information. The analysis of fixational eye movements can thus serve as a noninvasive means to detect agerelated or pathological changes in visual processing, which can in turn reflect associated cognitive or neurological disorders. However, the utility of such diagnostic approaches relies on the quality and usability of detection methods applied for the eye movement analysis. Here, we propose a novel method for (micro)saccade detection that is resistant to highfrequency recording noise, a frequent problem in videobased eye tracking in either aged subjects or subjects suffering from a vision-related pathology. The method is fast, it does not require manual noise removal, and it can work with position, velocity, or acceleration features, or a combination thereof. The detection accuracy of the proposed method is assessed on a new dataset of manually labeled recordings acquired from 14 subjects of advanced age (69-81 years old), performing an ocular fixation task. It is demonstrated that the detection accuracy of the new method compares favorably to that of two frequently used reference methods and that it is comparable to the best of the two algorithms when tested on an existing low-noise eye-tracking dataset.
The reported size of microsaccades is considerably larger today compared to the initial era of microsaccade studies during the 1950s and 1960s. We investigate whether this increase in size is related to the fact that the eye-trackers of today measure different ocular structures than the older techniques, and that the movements of these structures may differ during a microsaccade. In addition, we explore the impact such differences have on subsequent analyzes of the eye-tracker signals. In Experiment I, the movement of the pupil as well as the first and fourth Purkinje reflections were extracted from series of eye images recorded during a fixation task. Results show that the different ocular structures produce different microsaccade signatures. In Experiment II, we found that microsaccade amplitudes computed with a common detection algorithm were larger compared to those reported by two human experts. The main reason was that the overshoots were not systematically detected by the algorithm and therefore not accurately accounted for. We conclude that one reason to why the reported size of microsaccades has increased is due to the larger overshoots produced by the modern pupil-based eye-trackers compared to the systems used in the classical studies, in combination with the lack of a systematic algorithmic treatment of the overshoot. We hope that awareness of these discrepancies in microsaccade dynamics across eye structures will lead to more generally accepted definitions of microsaccades.
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