Automated nystagmus detection: Accuracy of slow-phase and quick-phase algorithms to determine the presence of nystagmus

PURPOSE

To verify the accuracy of automated nystagmus detection algorithms.

METHOD

Video-oculography (VOG) plots were analyzed from consecutive patients with dizziness presenting to a neurology clinic. Data were recorded for 30 s in upright position with fixation block. For automated nystagmus detection, slow-phase algorithm parameters included mean and median slow-phase velocity (SPV), and slow-phase duration ratio. Quick-phase algorithm parameters included saccadic difference and saccadic ratio. For verification, two independent blinded assessors reviewed VOG traces and videos and coded presence or absence of nystagmus. Assessor consensus was used as reference standard. Accuracy of slow-phase and quick-phase algorithm parameters were compared, and ROC analysis was performed.

RESULTS

Among 524 analyzed VOG traces, 99 were verified as nystagmus present and 425 were verified as nystagmus absent. Prevalence of nystagmus in the sample population was 18.9%. In ROC analysis, areas under the curve of individual algorithm parameters were 0.791-0.896. With optimal thresholds for determining presence or absence of nystagmus, algorithm sensitivity (70.7-87.9%), specificity (71.8-84.0%), and negative predictive value (91.7-96.4%) were ideal, but positive predictive value (38.8-53.4%) was not ideal. Combining algorithm parameters using logistic regression models mildly improved detection accuracy.

CONCLUSION

Both slow-phase and fast-phase algorithms were accurate for detecting nystagmus. Due to low positive predictive value, the utility of independent automated nystagmus detection systems is limited in clinical settings with low prevalence of nystagmus. Combining parameters using logistic regression models appears to improve detection accuracy, indicating that machine learning may potentially optimize the accuracy of future automated nystagmus detection systems.

1D Convolutional Neural Networks for Detecting Nystagmus

Vertigo is a type of dizziness characterised by the subjective feeling of movement despite being stationary. One in four individuals in the community experience symptoms of dizziness at any given time, and it can be challenging for clinicians to diagnose the underlying cause. When dizziness is the result of a malfunction in the inner-ear, the eyes flicker and this is called nystagmus. In this article we describe the first use of Deep Neural Network architectures applied to detecting nystagmus. The data used in these experiments was gathered during a clinical investigation of a novel medical device for recording head and eye movements. We describe methods for training networks using very limited amounts of training data, with an average of 11 mins of nystagmus across four subjects, and less than 24 hours of data in total, per subject. Our methods work by replicating and modifying existing samples to generate new data. In a cross-fold validation experiment, we achieve an average F1 score of 0.59 (SD = 0.24) across all four folds, showing that the methods employed are capable of identifying periods of nystagmus with a modest degree of accuracy. Notably, we were also able to identify periods of pathological nystagmus produced by a patient during an acute attack of Ménière's Disease, despite training the network on nystagmus that was induced by different means.

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

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.

An automated segmentation approach to calibrating infantile nystagmus waveforms

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.