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ToolsPhillips, John S., Newman, Jacob and Cox, Stephen (2021) Towards providing an automated approach to differentiating the nystagmus of Ménière’s Disease, Vestibular Migraine and Benign Paroxysmal Positional Vertigo. Otology & Neurotology, 42 (6). pp. 890-896. ISSN 1537-4505
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Abstract
Objective: The diagnosis of vertigo is challenging, particularly as patients usually present while asymptomatic. We have developed an ambulatory medical device that allows vestibular telemetry to record eye movements over a 30-day period to aid the diagnosis of vertigo. We have undertaken proof-of-concept work to identify unique properties of nystagmus that could be used to differentiate between three of the most common causes of vertigo: Ménière's disease, vestibular migraine, and Benign Paroxysmal Positional Vertigo. Patients: We analyze the nystagmus from patients with a diagnosis of Ménière's disease, vestibular migraine, and Benign Paroxysmal Positional Vertigo. Intervention(s): Our vestibular telemetry system includes a wearable, ambulatory monitor which continuously records horizontal and vertical eye-movements, as well as three-axis movements of the head. Main Outcome Measure(s): Horizontal and vertical eye-movement data, and three-axis head positioning data. Results: Sixteen participants were enrolled onto the study and three reported experiencing rotatory vertigo during their 30-day trial, confirmed by the presence of nystagmus in their eye-movement traces. Vestibular telemetry revealed distinct differences between the nystagmus produced during an acute Ménière's attack, and attacks of vestibular migraine and Benign Paroxysmal Positional Vertigo. Attack frequency, nystagmus duration, whether the nystagmus onset was motion provoked, nystagmus direction, slow phase velocity, and slow phase duration were found to be discriminatory features that could be exploited to allow an automated diagnosis to be made. Conclusions: The data provided by vestibular telemetry can be used to differentiate between different inner-ear causes of dizziness.
| Item Type: | Article |
|---|---|
| Faculty \ School: | Faculty of Medicine and Health Sciences > Norwich Medical School Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Centres > Population Health Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 15 Jan 2021 00:59 |
| Last Modified: | 10 Dec 2024 01:36 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/78185 |
| DOI: | 10.1097/MAO.0000000000003083 |
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