A Comparison of the SOFM with LVQ, SOFM without LVQ and Statistical Technique

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 a statistical pattern recognition techniquebased on Euclidean distance are presented.

Abstract

Abstract – The shapes and firing rates of MUAP’s (motor unit action potentials) in an EMG (electromyographic) 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. 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 Euclidean distance. A total of 521 MUAP’s obtained from 2 normal subjects, 4 subjects suffering from myopathy, and 5 subjects suffering from motor neuron disease were analyzed. The success rate for the ANN technique was 97.6%, the success rate for SOFM technique was 94.8%, and for statistical technique it was 95.3%. So SOFM technique along with LVQ is batter technology than the SOFM without LVQ technique and Statistical technique.