肌电图信号处理与肌肉疾病诊断

2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA) Pub Date : 2012-12-01 DOI:10.1109/ICTEA.2012.6462866

Onsy Abdul Alim, Dr. Mohamed Moselhy, Eng. Fatima Mroueh

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引用次数: 15

摘要

肌病(MYO)、神经病变(NEU)和正常(NOR)受试者的运动单位动作电位(MUAP)信号的实时记录，使用肌内肌电图(针肌电图)进行处理和处理，以便分类用于神经肌肉病理的诊断。采用前馈-反向传播神经网络进行分类。当使用时域特征作为神经网络的输入时，识别率高于70%。

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EMG signal processing and diagnostic of muscle diseases

Real time recordings of motor unit action potential (MUAP) signals from myopathy (MYO), neuropathy (NEU), and normal (NOR) subjects, using intramuscular electromyography (needle EMG) are treated and processed in order to be classified for the diagnosis of neuromuscular pathology. Feedforward-backpropagation neural network is used for the classification. Recognition rates were found to be higher than 70% and higher when using time domain features as inputs for the neural network.

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来源期刊

2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)

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