The electromyogram (EMG) in needle detection represents one of the steps of the electroneuromyogram (ENMG), an examination commonly performed in neurology. By inserting a needle into a muscle and studying the contraction during effort, the EMG provides extremely useful information on the functioning of the neuromuscular system of an individual, but it is an examination that remains complex to interpret. The objective of this work is to participate in the design and evaluation of a software allowing an automated analysis of EMG tracings of patients suspected of neuromuscular diseases, orienting the diagnosis towards either a neuropathic or myopathic process from recorded tracings. The software uses a method of signal decomposition according to a Markovian model, based on the analysis of motor unit potentials obtained by EMG, then a classification of the tracings. The tracings of 9 patients were thus analyzed and classified on the basis of the clinical interpretation of the neurologist, making it possible to initiate a "machine learning" process. The software will then be submitted to new tracings in order to test it against a practitioner experienced in EMG analysis.Translated with www.DeepL.com/Translator (free version)
翻译:针头检测中的电磁图(EMG)是神经学中常见的一种检查,即电脑神经图(ENMG)的一个步骤。通过在肌肉中插入针头和在努力中研究收缩,EMG提供了极有用的个人神经肌肉系统功能信息,但这种检查仍然很复杂,难以解释。这项工作的目的是参与设计和评价一个软件,以便对怀疑患有神经肌肉疾病的病人进行环球图追踪进行自动分析,将诊断导向记录跟踪的神经病理或近距离病理过程。该软件根据Markovian模型使用信号分解方法,该模型基于EMG对运动机组潜力的分析,然后对跟踪进行分类。因此,根据神经科的临床解释,对9名病人的追踪进行了分析和分类,从而有可能启动一个“机器学习”过程。该软件随后将提交给新的追踪系统,以测试在EMG分析过程中所经历的执业者。用 以 www.DeepL.com/Trefretelexal(www.tralated)/Trial(免费版本)测试该软件。