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首页> 外文期刊>Turkish Journal of Electrical Engineering and Computer Sciences >Comparison of AR parametric methods with subspace-based methods for EMG signal classification using stand-alone and merged neural network models
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Comparison of AR parametric methods with subspace-based methods for EMG signal classification using stand-alone and merged neural network models

机译：使用独立和合并的神经网络模型将AR参数方法与基于子空间的EMG信号分类方法进行比较

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This research introduces an electromyogram (EMG) pattern classification of individual motor unit action potentials (MUPs) from intramuscular electromyographic signals. The presented technique automatically classifies EMG patterns into healthy, myopathic, or neurogenic categories. To extract a feature vector from the EMG signal, we use different autoregressive (AR) parametric methods and subspace-based methods. The proposal was validated using EMG recordings composed of 1200 EMG patterns obtained from 7 healthy, 7 myopathic, and 13 neurogenic-disordered people. A feedforward error backpropagation artificial neural network (FEBANN) and combined neural network (CNN) were used for classification, where the success rate was slightly higher in CNN. Among the different AR and subspace methods used in this study, the highest performance was obtained with the eigenvector method. The following rates were the results achieved by using the CNN. The correct classification rate for EMG patterns was 97% for healthy, 93% for myopathic, and 92% for neurogenic patterns. The obtained accuracy for EMG signal classification is approximately 94% for CNN. The rates for FEBANN were as follows: 97% for healthy patterns, 92% for myopathic patterns, and 91% for neurogenic patterns. The obtained accuracy was 93.3%. By directly using raw EMG signals, EMG classifications of healthy, myopathic, or neurogenic classes are automatically addressed.
机译：这项研究介绍了从肌内肌电图信号对单个运动单位动作电位（MUP）进行肌电图（EMG）模式分类。提出的技术自动将EMG模式分类为健康，肌病或神经源性类别。为了从EMG信号中提取特征向量，我们使用了不同的自回归（AR）参数方法和基于子空间的方法。该提案已通过使用由7个健康，7个肌病和13个神经源性疾病患者获得的1200个EMG模式组成的EMG记录进行了验证。前馈误差反向传播人工神经网络（FEBANN）和组合神经网络（CNN）用于分类，其中CNN的成功率略高。在本研究中使用的不同AR和子空间方法中，特征向量方法获得了最高的性能。以下比率是使用CNN所获得的结果。 EMG模式的正确分类率为健康的97％，肌病的93％和神经源性的92％。对于CNN，获得的EMG信号分类精度约为94％。 FEBANN的发生率如下：健康模式为97％，肌病模式为92％，神经源性模式为91％。获得的准确度为93.3％。通过直接使用原始EMG信号，可以自动解决健康，肌病或神经源性类别的EMG分类。

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