Analysis of electrooculography signals for the detection of Myasthenia Gravis. Issue 11 (November 2019)

• Record Type: Journal Article
• Authors: Liang, Timothy; Boulos, Mark I.; Murray, Brian J.; Krishnan, Sridhar; Katzberg, Hans; Umapathy, Karthikeyan
• Is Part Of: Clinical neurophysiology. Volume 130:Issue 11(2019:Nov.)
• Page Start: 2105
• Page End: 2113
• Publication Date: 2019-11
• Subjects: Electrooculogram -- Myasthenia Gravis -- Sleep test -- Time domain analysis -- Wavelet domain analysis -- Pattern classification; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2019.08.008
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: A non-invasive tool for early stage Myasthenia Gravis (MG) screening. Quantification of eye movement characteristics elucidating ocular muscle impact of MG disorder. Wavelet analysis for detection of eye movement signal morphology relevant for MG classification. Abstract: Objective: A precursor to more severe forms of Myasthenia Gravis (MG) is ocular MG (OMG) in which the MG symptoms are localized to the eyes. Current MG diagnostic methods are often invasive, painful, and not always specific. The objective of the proposed work was to extract quantifiable features from electrooculography (EOG) signals recorded around the eyes and develop an alternative non-invasive screening method for detecting MG. Methods: EOG signals acquired from MG and Control subjects were analyzed for eye movement characteristics and quantified using time and wavelet domain signal processing techniques. The ability of the proposed approaches to classify MG vs. control subjects was evaluated using a linear discriminant analysis (LDA) based classifier. Results: The range of overall classification accuracies achieved by the proposed time and wavelet domain approaches for different groupings were between 82.1–83.3% (Rise Rate feature: P < 0.01, AUC ≥ 0.87) and 82.1–87.2% (Mean Scale Band Energy feature: P < 0.01, AUC ≥ 0.89), respectively. Conclusion: Our results demonstrate that an EOG-based signal analysis is a potentially viable non-invasive alternative for MG screening. Significance: The proposed approach could lead to early detection of MG and thereby improve clinical outcomes in this population.