Characterisation of baseline oscillation in congenital nystagmus eye movement recordings

Video-oculography (VOG) is a non-invasive method used for the eye tracking detecting. A most important work of VOG is to accurately estimate the pupil center. However, VOG cannot acquire eye movement when the eye blinks. A Grey forecasting model was proposed to predict the pupil center for the blinking frame of VOG video. The Grey forecasting model used GM(1,1) model to carry on the modeling. Then an experiment is provided. The experiment results show that the predicted data of the Grey forecasting meet the characteristics of the patients with nystagmus. The Grey forecasting model is a viable means of accurately predict the pupil center for blinking in VOG.

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“…) That is called the shadow equation of the GM (1, 1) model equation (6). The solution of equation ( 7) is (1) (1)…”

Section: Grey Forecasting Modelmentioning

confidence: 99%

“…Eye movement technology is widely applied to various fields, including nystagmus analysis [1], psychology [2], nerve [3], astronautics [4], medicine judgment [5], advertisement [6][7] and so on. The aim of the technology is to carry out the eye tracing detection.…”

Section: Introductionmentioning

confidence: 99%

Grey Forecasting Model for Patching Blinking in Video-Oculography

Chen

Yang

et al. 2011

AMM

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“…The key characteristics of IN include: the oscillation plane, the waveform amplitude and period, the slow and fast phase, the foveation window and the baseline oscillation (see Additional file 1: Figure S1) [10, 11]. Typically, a slow eye movement (the slow phase) takes the eye away from the target, and a fast eye movement (the fast phase) returns it to the target.…”

Section: Introductionmentioning

confidence: 99%

“…An important characteristic of the nystagmus oscillation is the foveation window: this is the cycle interval within which the velocity of the eyes is low enough (< 4 deg/s) for the patient to be able to see clearly [12]. The baseline oscillation is a low frequency sinusoidal oscillation, the amplitude of which is correlated with the nystagmus amplitude [10]. …”

Section: Introductionmentioning

confidence: 99%

“…Their methods allowed them to investigate: (i) the dynamic latency of a saccadic neuron; (ii) the prediction capacity of a given model; (iii) whether a more complex model increased the prediction capacity of the model; and (iv) the relationship between the initial conditions and saccade trajectories. Pasquariello et al [10] used parameter optimisation to fit a model to experimental recordings of the infantile nystagmus baseline oscillation. Their results showed that, on average, the amplitude of the baseline oscillation was half that of the corresponding nystagmus oscillation.…”

Section: Introductionmentioning

confidence: 99%

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Optimisation of an exemplar oculomotor model using multi-objective genetic algorithms executed on a GPU-CPU combination

Avramidis

Akman

2017

BMC Syst Biol

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BackgroundParameter optimisation is a critical step in the construction of computational biology models. In eye movement research, computational models are increasingly important to understanding the mechanistic basis of normal and abnormal behaviour. In this study, we considered an existing neurobiological model of fast eye movements (saccades), capable of generating realistic simulations of: (i) normal horizontal saccades; and (ii) infantile nystagmus – pathological ocular oscillations that can be subdivided into different waveform classes. By developing appropriate fitness functions, we optimised the model to existing experimental saccade and nystagmus data, using a well-established multi-objective genetic algorithm. This algorithm required the model to be numerically integrated for very large numbers of parameter combinations. To address this computational bottleneck, we implemented a master-slave parallelisation, in which the model integrations were distributed across the compute units of a GPU, under the control of a CPU.ResultsWhile previous nystagmus fitting has been based on reproducing qualitative waveform characteristics, our optimisation protocol enabled us to perform the first direct fits of a model to experimental recordings. The fits to normal eye movements showed that although saccades of different amplitudes can be accurately simulated by individual parameter sets, a single set capable of fitting all amplitudes simultaneously cannot be determined. The fits to nystagmus oscillations systematically identified the parameter regimes in which the model can reproduce a number of canonical nystagmus waveforms to a high accuracy, whilst also identifying some waveforms that the model cannot simulate. Using a GPU to perform the model integrations yielded a speedup of around 20 compared to a high-end CPU.ConclusionsThe results of both optimisation problems enabled us to quantify the predictive capacity of the model, suggesting specific modifications that could expand its repertoire of simulated behaviours. In addition, the optimal parameter distributions we obtained were consistent with previous computational studies that had proposed the saccadic braking signal to be the origin of the instability preceding the development of infantile nystagmus oscillations. Finally, the master-slave parallelisation method we developed to accelerate the optimisation process can be readily adapted to fit other highly parametrised computational biology models to experimental data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0416-2) contains supplementary material, which is available to authorized users.

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