Classification of MUAP’s by using ANN pattern reorganization technique

Anjana Bhardwaj^*, Manish ^**,   A. K. Arora^***

^*(Department of Electronics and Communication Engineering, ABES Engineering College, Ghaziabad

^**(Department of Electronics and Communication Engineering, ABES Engineering College, Ghaziabad

^***(Department of Electronics and Communication Engineering, ABES Engineering College, Ghaziabad

Corresponding author Email: anjana.13bhardwaj@gmail.com

Accepted 12 July 2012

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 a pattern recognition techniques is present which is 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). 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%.

Keywords: Artificial Neural Network, Electromyography, learning vector quantization, Motor unit Action Potentials, Self-organizing feature maps.