Reconstructing animated eye movements from electrooculography data to aid the diagnosis of vestibular disorders

Newman, Jacob L., Phillips, John S. and Cox, Stephen J. (2022) Reconstructing animated eye movements from electrooculography data to aid the diagnosis of vestibular disorders. International Journal of Audiology, 61 (1). pp. 78-83. ISSN 1499-2027

Preview

PDF (Accepted_Manuscript) - Accepted Version Available under License Creative Commons Attribution. Download (819kB) | Preview

Abstract

Objective: To develop a method of visualising electrooculography data to improve the interpretability of nystagmus eye-movements captured using the Continuous Ambulatory Vestibular Assessment (CAVA®) device. Design: We are currently undertaking a clinical investigation to evaluate the capabilities of the CAVA® device to detect periods of pathological nystagmus. The work presented here was undertaken using unblinded data obtained from the preliminary phase of this investigation. Study sample: One patient with Ménière’s disease and one with Benign Paroxysmal Positional Vertigo. Results: Using the electrooculography data captured by the CAVA® device, we reconstructed 2D animations of patients’ eye movements during attacks of vertigo. We were able to reanimate nystagmus produced as a consequence of two conditions. Concurrent video footage showed that the animations were visually very similar to the patient’s actual eye-movements, excepting torsional eye-movements. Conclusions: The reconstructed animations provide an alternative presentation modality, enabling clinicians to largely interpret electrooculography data as if they were present during a vertigo attack. We were able to recreate nystagmus from attacks experienced in the community rather than a clinical setting. This information provides an objective record of a patient’s nystagmus and could be used to complement a full neurotologic history when considering diagnosis and treatment options.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups:	Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Medicine and Health Sciences > Research Centres > Population Health Faculty of Science > Research Groups > Data Science and AI
Depositing User:	LivePure Connector
Date Deposited:	26 Jan 2021 00:56
Last Modified:	16 Dec 2024 01:33
URI:	https://ueaeprints.uea.ac.uk/id/eprint/78280
DOI:	10.1080/14992027.2021.1883196

Downloads

Actions (login required)

View Item