D{\'e}composition et analyse de trac{\'e}s EMG pour aider au diagnostic des maladies neuromusculaires

TL;DR

This paper presents a software tool that automates the analysis of EMG tracings to assist in diagnosing neuromuscular diseases by decomposing signals and classifying them as neuropathic or myopathic, based on a Markovian model.

Contribution

It introduces a novel automated method for EMG signal decomposition and classification using a Markovian model to aid neuromuscular disease diagnosis.

Findings

Analyzed and classified 9 patient tracings with clinical interpretation.

Developed a machine learning process for EMG analysis.

Software to be tested on new tracings for validation.

Abstract

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)