Waveform type evaluation in congenital nystagmus

Pasquariello, G.; Cesarelli, Mario; Romano, Maria; Gatta, Antonio La; Bifulco, Paolo; Fratini, Antonio

doi:10.1016/j.cmpb.2010.02.006

Cited by 11 publications

(7 citation statements)

References 30 publications

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“…The primary axis of nystagmus (i.e., horizontal or vertical) is determined by finding the axis with the highest standard deviation of the position signal. The waveform is next split into cycles, using the method described by Pasquariello et al (2010), which finds peaks in the waveform. Quick phases (saccades) are then detected using the algorithm designed by Pander, Czabański, Przybyła, and Pojda-Wilczek (2014).…”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%

An automated segmentation approach to calibrating infantile nystagmus waveforms

et al. 2019

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“…After smoothing, an adjustable threshold was set for the screen, and the filtered peak was found. The adjacent maximum/minimum values were found according to the signal position (Figure 9) [19] The Fourier transform was then used to find the frequency (3) at which FP occurred. High-pass and band-pass FIR filtering were adopted for peak detection.…”

Section: Comparisons With the State-of-the-art Approachesmentioning

confidence: 99%

Section: Comparisons With the State-of-the-art Approachesmentioning

confidence: 99%

“…However, this method is prone to noise interference and can cause serious misjudgment, resulting in low accuracy. The application of the Fourier transform and filtering [19] depends on the judgment of values, which requires experienced researchers to select and set. Among them, high-pass filtering is the easiest to select, so the FP position can be correctly determined.…”

Section: Comparisons With the State-of-the-art Approachesmentioning

confidence: 99%

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A Fast and Effective System for Analysis of Optokinetic Waveforms with a Low-Cost Eye Tracking Device

2020

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Optokinetic nystagmus (OKN) is an involuntary eye movement induced by motion of a large proportion of the visual field. It consists of a “slow phase (SP)” with eye movements in the same direction as the movement of the pattern and a “fast phase (FP)” with saccadic eye movements in the opposite direction. Study of OKN can reveal valuable information in ophthalmology, neurology and psychology. However, the current commercially available high-resolution and research-grade eye tracker is usually expensive. Methods & Results: We developed a novel fast and effective system combined with a low-cost eye tracking device to accurately quantitatively measure OKN eye movement. Conclusions: The experimental results indicate that the proposed method achieves fast and promising results in comparisons with several traditional approaches.

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“…CGN appears usually in early infancy and results in degrade vision and illusory motion of the visual field. CGN is a symptom of neurological disorder but its pathogenesis is still unknown [3,8]. The optokinetic nystagmus is characterized as involuntary eye movement response when moving stimulus in a large visual field is presented [11].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Approach to Saccades Detection in Optokinetic Nystagmus

Czabański

Pander

Przybyła

2014

Advances in Intelligent Systems and Computing

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Abstract. The electronystagmography (ENG) based analysis of the nystagmus provides valuable information about condition of the human vision system. The typical ENG signal corresponding to nystagmus has a form of a saw tooth waveform with slow and fast (saccade) components. The slow component is related to the stimulus while the saccade refers to a rapid reset of eye position. The accurate detection of saccadic eye movements is inevitable when determining the nystagmus characteristic. The paper presents saccades detection method that is based on fuzzy clustering. The proposed procedure is computationally efficient and allows for precise determination of the saccade position in the time domain.

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Waveform type evaluation in congenital nystagmus

Cited by 11 publications

References 30 publications

An automated segmentation approach to calibrating infantile nystagmus waveforms

An automated segmentation approach to calibrating infantile nystagmus waveforms

A Fast and Effective System for Analysis of Optokinetic Waveforms with a Low-Cost Eye Tracking Device

Fuzzy Approach to Saccades Detection in Optokinetic Nystagmus

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