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Related Experiment Video

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Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
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Automatic nystagmus detection and quantification in long-term continuous eye-movement data.

Jacob L Newman1, John S Phillips2, Stephen J Cox1

  • 1School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, United Kingdom.

Computers in Biology and Medicine
|October 3, 2019
PubMed
Summary
This summary is machine-generated.

New algorithms accurately detect nystagmus, an abnormal eye movement linked to dizziness, using continuous eye-tracking data. This breakthrough aids in diagnosing inner-ear disorders and understanding dizziness attacks in older adults.

Keywords:
Biomedical signal processingDizzinessElectronystagmographyEvent detectionNystagmusTime series classificationVestibular diseases

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Ophthalmology

Background:

  • Dizziness and imbalance are common in individuals over 65, often episodic and difficult to diagnose.
  • Inner-ear malfunctions frequently cause dizziness accompanied by nystagmus, abnormal eye movements.
  • The Continuous Ambulatory Vestibular Assessment (CAVA) device offers continuous eye-movement monitoring for dizziness insights.

Purpose of the Study:

  • To develop and validate novel algorithms for detecting short periods of induced nystagmus from CAVA device data.
  • To assess the accuracy of these algorithms in identifying nystagmus during controlled conditions.
  • To compare computational analysis of nystagmus with conventional caloric testing methods.

Main Methods:

  • Novel algorithms were designed to analyze long-term eye movement data from the CAVA device.
  • A blinded trial involved 17 healthy subjects artificially inducing nystagmus via VR headset.
  • Data from induced nystagmus was collected and compared against conventional nystagmus assessment techniques.

Main Results:

  • The developed algorithms achieved 98.77% accuracy in detecting short periods of artificially induced nystagmus.
  • The study successfully identified and quantified various types of nystagmus using computational analysis.
  • Results demonstrated the potential for CAVA device data to aid in vestibular disorder diagnosis.

Conclusions:

  • Novel algorithms effectively detect induced nystagmus from continuous eye-movement data.
  • The CAVA device and computational analysis offer a promising approach for diagnosing dizziness and inner-ear disorders.
  • This technology can provide objective, quantifiable data for vestibular function assessment.