Non-invasive Classification of Myasthenia Gravis and Other Ocular Disorders Using Electrooculogram Features (P1-11.008)
(1) To use electrooculogram (EOG) to differentiate patients with Myasthenia Gravis (MG) from those with extra-ocular movement abnormalities due to other etiologies (non-MG group). (2) To test the sensitivity of EOG features to classifying the EOG data into Controls (free of extra-ocular abnormalities), MG patients, and a non-MG group.
Authors
- Arun N. E. Sundaram (ORCID: https://orcid.org/0009-0005-8890-0832)
- Hiten G. Sheth
- Todd Le
- Kevin E. Thorpe (ORCID: https://orcid.org/0000-0002-7586-3893)
- Karl Magtibay
- Brian J. Murray (ORCID: https://orcid.org/0000-0001-5085-0909)
- Hans Katzberg (ORCID: https://orcid.org/0000-0002-4716-3304)
- Sarah Berger (ORCID: https://orcid.org/0000-0002-6370-9397)
- Dingquan Yang (ORCID: https://orcid.org/0000-0002-3421-1602)
- Sridhar Krishnan (ORCID: https://orcid.org/0000-0002-4659-564X)
- Mark I. Boulos (ORCID: https://orcid.org/0000-0002-9547-1889)
- Lahiru Fernando
- Karthi Umapathi
- Mona Irannejad
Institutions
- Sunnybrook Health Science Centre (CA)
- University Health Network (CA)
- University of Toronto (CA)
- Health Sciences Centre (CA)
- Health Net (US)
- Toronto Metropolitan University (CA)
Publication Details
- Journal
- Neurology
- Published
- 2025-04-07
- DOI
- https://doi.org/10.1212/wnl.0000000000209072
- Citations
- 1
- Primary Topic
- Myasthenia Gravis and Thymoma
- Type
- article
- Field-Weighted Citation Impact
- 1.39