Mesures d'évaluation de la myasthénie Gravis: Incorporation à la pratique clinique

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Mesures d'évaluation de la myasthénie Gravis: Incorporation à la pratique clinique

Message par Pboulanger » 26 févr. 2017 11:36

:hi:

Lu sur https://www.ncbi.nlm.nih.gov/pubmed/28226641


Traduction disponible directement en cliquant en bas à droite de ce message sur notre forum

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Outcome Measures in Myasthenia Gravis: Incorporation Into Clinical Practic
Liang T, Boulos MI, Murray BJ, Krishnan S, Katzberg H, Umapathy K, Liang T, Boulos MI, Murray BJ, Krishnan S, Katzberg H, Umapathy K, Boulos MI, Umapathy K, Katzberg H, Krishnan S, Liang T, Murray BJ.
Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:896-899.
doi: 10.1109/EMBC.2016.7590845.


Abstract

Myasthenia gravis (MG) is an autoimmune neuromuscular disorder resulting from skeletal muscle weakness and fatigue. An early common symptom is fatigableweakness of the extrinsic ocular muscles; if symptoms remain confined to theocular muscles after a few years, this is classified as ocular myasthenia gravis (OMG).

Diagnosis of MG when there are mild, isolated ocular symptoms can be difficult, and currently available diagnostic techniques are insensitive, non-specific or technically cumbersome. In addition, there are no accurate biomarkers to follow severity of ocular dysfunction in MG over time.

Single-fiber electromyography (SFEMG) and repetitive nerve stimulation (RNS) offers a way of detecting and measuring ocular muscle dysfunction in MG, however, challenges of these methods include a poor signal to noise ratio in quantifying eye muscle weakness especially in mild cases.

This paper presents one of the attempts to use the electric potentials from the eyes or electrooculography (EOG) signals but obtained from three different forms of sleep testing to differentiate MG patients from age- and gender-matched controls.

We analyzed 8 MG patients and 8 control patients and demonstrated a difference in the average eye movements detected between the groups.

A classification accuracy as high as 68.8% was achieved using a linear discriminant analysis based classifier.
Amicalement,
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