An Optokinetic Nystagmus Detection Method for Use With Young Children

Abstract

The detection of vision problems in early childhood can prevent neurodevelopmental disorders such as amblyopia. However, accurate clinical assessment of visual function in young children is challenging. optokinetic nystagmus (OKN) is a reflexive sawtooth motion of the eye that occurs in response to drifting stimuli, that may allow for objective measurement of visual function in young children if appropriate child-friendly eye tracking techniques are available. In this paper, we present offline tools to detect the presence and direction of the optokinetic reflex in children using consumer grade video equipment. Our methods are tested on video footage of children (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$N = 5$ \end{document} children and 20 trials) taken as they freely observed visual stimuli that induced horizontal OKN. Using results from an experienced observer as a baseline, we found the sensitivity and specificity of our OKN detection method to be 89.13% and 98.54%, respectively, across all trials. Our OKN detection results also compared well (85%) with results obtained from a clinically trained assessor. In conclusion, our results suggest that OKN presence and direction can be measured objectively in children using consumer grade equipment, and readily implementable algorithms.

1D Convolutional Neural Networks for Detecting Nystagmus

“…Although neural networks have previously been applied to several tasks involving eye-movement signals, such as classifying normal versus abnormal nystagmus during caloric tests [16] and detecting saccades [34], this study is the first example of 1D CNNs applied to the task of detecting entire nystagmus waveforms from within hours of normal eye-movement data. While heuristic approaches to detecting optokinetic nystagmus have been shown to yield high levels of classification accuracy (89.13% sensitivity and 98.54% specificity in [10], and 93% accuracy in [12]), these results are not comparable with our study as the data was captured during optokinetic tests and are extremely short in duration (8 seconds each in [10], compared to up to 24 hours in our longest example and almost an hour in the shortest). While it is impressive that [10] were able to extract and analyse eye-movement signals from young children in a laboratory setting, the constrained detection task described is very different to identifying nystagmus within many hours of eye-movement data.…”

“…While heuristic approaches to detecting optokinetic nystagmus have been shown to yield high levels of classification accuracy (89.13% sensitivity and 98.54% specificity in [10], and 93% accuracy in [12]), these results are not comparable with our study as the data was captured during optokinetic tests and are extremely short in duration (8 seconds each in [10], compared to up to 24 hours in our longest example and almost an hour in the shortest). While it is impressive that [10] were able to extract and analyse eye-movement signals from young children in a laboratory setting, the constrained detection task described is very different to identifying nystagmus within many hours of eye-movement data.…”

“…Apart from our previous work in [9], there are no previous studies that specifically focus on detecting nystagmus within long-term electrooculography data. However, several algorithms have been developed to identify nystagmus within short-term data [10]- [15]. Many of these systems adopt a heuristic approach to nystagmus detection, usually involving the identification of peaks in signal velocity, which can indicate the presence of a fast phase.…”

<p>The Detection of Spontaneous Venous Pulsation with Smartphone Video Ophthalmoscopy</p>

“…Current applications of the automatic detection of dynamic clinical signs include the smart phone pupillometer and optokinetic nystagmus detection. [19][20][21] We trialed the current videos with the slit lamp gradings using an open source platform for deep learning. We used the deep learning framework Inception V3 by Tensorflow (Alphabet Inc, CA, USA).…”

gazeNet: End-to-end eye-movement event detection with deep neural networks

“…Besides fixations, saccades and PSOs, there are plenty of other events in eye-movement data waiting to be detected algorithmically. Nystagmus and smooth pursuit are classic problems for feature-based algorithms, with only a few specifically tailored algorithms existing that are able to deal with these events (see for example Komogortsev & Karpov, 2013;Larsson et al, 2013Larsson et al, , 2015 for pursuit and Juhola (1988), Turuwhenua, Yu, Mazharullah, and Thompson (2014) and Sangi, Thompson, and Turuwhenua, (2015) for nystagmus detection). A major challenge to the field is to produce an algorithm that codes events from head-mounted eye trackers, or eye trackers integrated in head-mounted display used for virtual reality, given that the data recorded with such systems contains a rich mixture of head and eye movements.…”