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A comprehensive electrooculography measurement system and protocols for screening myasthenia gravis

delete2026-02-01
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OA
AI
L
Le, Tien Loc *
B
Boulos, Mark I.
Y
Yang, Donna
B
Berger, Sarah
I
Irannejad, Mona
F
Figueroa, Cesar Morales
T
Thorpe, Kevin E.
M
Murray, Brian
K
Krishnan, Sridhar
S
Sundaram, Arun N. E.
K
Katzberg, Hans
U
Umapathy, Karthikeyan
DOI:10.1016/j.mex.2026.103810delete
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Abstract

Abstract

En 中文
Myasthenia gravis (MG) is a neuromuscular disorder that can precipitate serious and fatal complications, especially when respiratory muscles are affected. Current electrodiagnostic methods, such as Single Fiber Electromyography (SFEMG) and Repetitive Nerve Stimulation (RNS), have notable limitations. While SFEMG is sensitive to neuromuscular abnormalities rather than being specific for MG, it is also a semi-invasive procedure that requires supervision by specialist clinicians and an expensive clinical setup. Conversely, RNS is highly specific for MG, but its sensitivity decreases in cases of low severity. Prior studies, including our previous work, have demonstrated that the manifestation of MG in affecting eye movements can be indirectly quantified using electrooculography (EOG) signals. This non-invasive method could develop into a widely used and easily deployed screening method, especially for early detection of MG using readily obtainable biomarkers. With this goal in mind, our research team developed data collection protocols using a standardized eye movement protocol that could be implemented in a standard clinical setting to acquire EOG data. In this article, we present the experimental setup, test protocols, sample outcomes, and preliminary analysis approaches. Common artifacts encountered, as well as technical and logistical challenges associated with such a setup, are also discussed. 1. An approach to non-invasively detect and quantify MG using eye movement signals. 2. Data collection protocols for acquiring eye movement signals designed for MG quantification 3. Novel eye movement-based biomarkers in quantifying MG.
Keywords:
Signal processing
Signal acquisition
Electrooculogram
Electrooculogram protocols
Myasthenia gravis
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MethodsX cover
MethodsX
IF:
1.9
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S
sunnybrook health science center
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Sunnybrook Research Institute
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toronto metropolitan university
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university of toronto
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