Christodoulou C I, Pattichis C S
Department of Electronic Engineering, Queen Mary and Westfield College, University of London, U.K.
IEEE Trans Biomed Eng. 1999 Feb;46(2):169-78. doi: 10.1109/10.740879.
The shapes and firing rates of motor unit action potentials (MUAP's) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. In order to extract this information from EMG signals recorded at low to moderate force levels, it is required: i) to identify the MUAP's composing the EMG signal, ii) to classify MUAP's with similar shape, and iii) to decompose the superimposed MUAP waveforms into their constituent MUAP's. For the classification of MUAP's two different pattern recognition techniques are presented: i) an artificial neural network (ANN) technique based on unsupervised learning, using a modified version of the self-organizing feature maps (SOFM) algorithm and learning vector quantization (LVQ) and ii) a statistical pattern recognition technique based on the Euclidean distance. A total of 1213 MUAP's obtained from 12 normal subjects, 13 subjects suffering from myopathy, and 15 subjects suffering from motor neuron disease were analyzed. The success rate for the ANN technique was 97.6% and for the statistical technique 95.3%. For the decomposition of the superimposed waveforms, a technique using crosscorrelation for MUAP's alignment, and a combination of Euclidean distance and area measures in order to classify the decomposed waveforms is presented. The success rate for the decomposition procedure was 90%.
肌电图（EMG）信号中运动单位动作电位（MUAP）的形状和发放频率为神经肌肉疾病的诊断提供了重要的信息来源。为了从低至中等用力水平记录的EMG信号中提取这些信息，需要：i）识别构成EMG信号的MUAP；ii）对形状相似的MUAP进行分类；iii）将叠加的MUAP波形分解为其组成的MUAP。对于MUAP的分类，提出了两种不同的模式识别技术：i）基于无监督学习的人工神经网络（ANN）技术，使用自组织特征映射（SOFM）算法和学习矢量量化（LVQ）的改进版本；ii）基于欧几里得距离的统计模式识别技术。共分析了从12名正常受试者、13名患有肌病的受试者和15名患有运动神经元疾病的受试者获得的1213个MUAP。ANN技术的成功率为97.6%，统计技术的成功率为95.3%。对于叠加波形的分解，提出了一种使用互相关进行MUAP对齐的技术，以及一种结合欧几里得距离和面积测量来对分解波形进行分类的技术。分解过程的成功率为90%。
