Modeling and quality assessment of nystagmus eye movements recorded using an eye-tracker

Multifractal characterization of nystagmus eye movements

In this work, we investigate the multifractal properties of eye movement dynamics of children with infantile nystagmus, particularly the fluctuations of its velocity. The eye movements of three children and one adult with infantile nystagmus were evaluated in a simple task in comparison with 28 children with no ocular pathologies. Four indices emerge from the analysis: the classical Hurst exponent, the singularity strength corresponding to the maximum of the singularity spectrum, the asymmetry of the singularity spectrum, and the multifractal strength, each of which characterizes a particular aspect of eye movement dynamics. Our findings indicate that, when compared to children with no ocular pathologies, patients with infantile nystagmus present lower values of all indices. Except for the multifractal strength, the difference in the remaining indices is statistically significant. To test whether the characterization of patients with infantile nystagmus in terms of multifractality indices allows them to be distinguished from children without ocular pathologies, we performed an unsupervised clustering analysis and classified the subjects using supervised clustering techniques. The results indicate that these indices do, indeed, distinctively characterize the eye movements of patients with infantile nystagmus.

A low-cost robotic oculomotor simulator for assessing eye tracking accuracy in health and disease

Eye tracking accuracy is affected in individuals with vision and oculomotor deficits, impeding our ability to answer important scientific and clinical questions about these disorders. It is difficult to disambiguate decreases in eye movement accuracy and changes in accuracy of the eye tracking itself. We propose the EyeRobot-a low-cost, robotic oculomotor simulator capable of emulating healthy and compromised eye movements to provide ground truth assessment of eye tracker performance, and how different aspects of oculomotor deficits might affect tracking accuracy and performance. The device can operate with eccentric optical axes or large deviations between the eyes, as well as simulate oculomotor pathologies, such as large fixational instabilities. We find that our design can provide accurate eye movements for both central and eccentric viewing conditions, which can be tracked by using a head-mounted eye tracker, Pupil Core. As proof of concept, we examine the effects of eccentric fixation on calibration accuracy and find that Pupil Core's existing eye tracking algorithm is robust to large fixation offsets. In addition, we demonstrate that the EyeRobot can simulate realistic eye movements like saccades and smooth pursuit that can be tracked using video-based eye tracking. These tests suggest that the EyeRobot, an easy to build and flexible tool, can aid with eye tracking validation and future algorithm development in healthy and compromised vision.

Determining Which Sine Wave Frequencies Correspond to Signal and Which Correspond to Noise in Eye-Tracking Time-Series

The Fourier theorem states that any time-series can be decomposed into a set of sinusoidal frequencies, each with its own phase and amplitude. The literature suggests that some frequencies are important to reproduce key qualities of eye-movements ("signal") and some of frequencies are not important ("noise"). To investigate what is signal and what is noise, we analyzed our dataset in three ways: (1) visual inspection of plots of saccade, microsaccade and smooth pursuit exemplars; (2) analysis of the percentage of variance accounted for (PVAF) in 1,033 unfiltered saccade trajectories by each frequency band; (3) analyzing the main sequence relationship between saccade peak velocity and amplitude, based on a power law fit. Visual inspection suggested that frequencies up to 75 Hz are required to represent microsaccades. Our PVAF analysis indicated that signals in the 0-25 Hz band account for nearly 100% of the variance in saccade trajectories. Power law coefficients (a, b) return to unfiltered levels for signals low-pass filtered at 75 Hz or higher. We conclude that to maintain eyemovement signal and reduce noise, a cutoff frequency of 75 Hz is appropriate. We explain why, given this finding, a minimum sampling rate of 750 Hz is suggested.

Electrophysiological analysis of ENG signals in patients with Covid-19

Background

Currently, there is an increasing number of patients reporting dizziness, which has resulted in a positive COVID-19 PCR test. In this paper, we analyzed the ENG signals recorded from patients with a positive COVID-19 PCR test.

Methods

In this paper, both linear and nonlinear analyses of time series were employed to determine the regularity and complexity of a recorded ENG signal.

Results

The Wilcoxon rank-sum test indicated that the COVID-19 and non-COVID groups have significant differences based on different extracted features. Various machine learning methods including Linear Discriminant Analysis (LDA), Naïve Base (NB), K-nearest Neighbours (KNN), and Support Vector Machines (SVM) were used to classify COVID-19 and non-COVID groups. The best accuracy, precision and FCR achieved by SVM are 86%, 91% and 0.13.

Conclusion

In this study, ENG signals were recorded from COVID-19 and control groups. Linear and non-linear features were extracted from the recorded signals to identify significantly different features. Subjects were classified based on SVM and different classifiers. The SVM (polynomial kernel) classifier showed the best result. The proposed method had not been used for the classification of COVID-19 and non-COVID-19 subjects before. This work helps other researchers conduct more research on the development of machine learning methods to diagnose the COVID-19 virus using ENG and other physiological signals.

Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review

Mild cognitive impairment (MCI), representing the 'transitional zone' between normal cognition and dementia, has become a novel topic in clinical research. Although early detection is crucial, it remains logistically challenging at the same time. While traditional pen-and-paper tests require in-depth training to ensure standardized administration and accurate interpretation of findings, significant technological advancements are leading to the development of procedures for the early detection of Alzheimer's disease (AD) and facilitating the diagnostic process. Some of the diagnostic protocols, however, show significant limitations that hamper their widespread adoption. Concerns about the social and economic implications of the increasing incidence of AD underline the need for reliable, non-invasive, cost-effective, and timely cognitive scoring methodologies. For instance, modern clinical studies report significant oculomotor impairments among patients with MCI, who perform poorly in visual paired-comparison tasks by ascribing less attentional resources to novel stimuli. To accelerate the Global Action Plan on the Public Health Response to Dementia 2017-2025, this work provides an overview of research on saccadic and exploratory eye-movement deficits among older adults with MCI. The review protocol was drafted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases were systematically searched to identify peer-reviewed articles published between 2017 and 2022 that examined visual processing in older adults with MCI and reported gaze parameters as potential biomarkers. Moreover, following the contemporary trend for remote healthcare technologies, we reviewed studies that implemented non-commercial eye-tracking instrumentation in order to detect information processing impairments among the MCI population. Based on the gathered literature, eye-tracking-based paradigms may ameliorate the screening limitations of traditional cognitive assessments and contribute to early AD detection. However, in order to translate the findings pertaining to abnormal gaze behavior into clinical applications, it is imperative to conduct longitudinal investigations in both laboratory-based and ecologically valid settings.

A dynamical method to objectively assess infantile nystagmus based on eye tracking. A pilot study

PURPOSE

The purpose of this research is to propose a new method for the easy, inexpensive and objective quantification of nystagmus using eye-tracking records collected during a simple reading task that could be implantable in clinical practice to assess patients with nystagmus.

METHODS

This is a prospective, observational pilot study. Eye movements of 4 nystagmus patients and 9 healthy children during a reading task (a paragraph with 82 words) on a 15'' monitor were collected and compared. Data are time series indicating the gaze position on the screen. Two quantifiers were proposed: IndS (based on the speed of movements) and IndF (based on the variation of the gaze trajectory).

RESULTS

The indices proposed reflect differences in the behavior of eye movements between the two groups. Nystagmus patients present higher values of IndS - indicating smaller number of slow movements (16% of movements with speeds <0.33 1/s for nystagmus and 85% for the control group, with p = 0.01) - and higher values of IndF - indicating higher gaze fluctuation (p = 0.01). Differences were not related with reading speed as show the mean and standard deviation: the nystagmus group required 115±45 s to complete the task and the control group 151±85 s; p = 0.73.

CONCLUSIONS

The proposed indices provide a new method that allows an objective assessment of nystagmus, with potential use in clinical and research practice to improve the follow-up of patients by monitoring the nystagmus over time or treatment.

Analysing nystagmus waveforms: a computational framework

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.

Waveform characterisation and comparison of nystagmus eye-tracking signals

OBJECTIVE

Pathological nystagmus is a symptom of oculomotor disease where the eyes oscillate involuntarily. The underlying cause of the nystagmus and the characteristics of the oscillatory eye movements are patient specific. An important part of clinical assessment in nystagmus patients is therefore to characterise different recorded eye-tracking signals, i.e. waveforms.

APPROACH

A method for characterisation of the nystagmus waveform morphology is proposed. The method extracts local morphologic characteristics based on a sinusoidal model, and clusters these into a description of the complete signal. The clusters are used to characterise and compare recordings within and between patients and tasks. New metrics are proposed that can measure waveform similarity at different scales; from short signal segments up to entire signals, both within and between patients.

MAIN RESULTS

The results show that the proposed method robustly can find the most prominent nystagmus waveforms in a recording. The method accurately identifies different eye movement patterns within and between patients and across different tasks.

SIGNIFICANCE

In conclusion, by allowing characterisation and comparison of nystagmus waveform patterns, the proposed method opens up for investigation and identification of the underlying condition in the individual patient, and for quantifying eye movements during tasks.