Comparison of the techniques used for sgmentation of EMG signals

  • Authors:

  • Gurmanik Kaur;Ajat Shatru Arora;V. K. Jain

  • Affiliations:

  • EIE Department, SLIET, Longowal, Sangrur, Punjab, India;EIE Department, SLIET, Longowal, Sangrur, Punjab, India;EIE Department, SLIET, Longowal, Sangrur, Punjab, India

  • Venue:
  • MACMESE'09 Proceedings of the 11th WSEAS international conference on Mathematical and computational methods in science and engineering
  • Year:
  • 2009

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Abstract

The shapes of motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. In order to extract this information from the EMG signals recorded at low to moderate force levels, segmentation is required to identify the MUAPs composed by the EMG signal. In this work, three techniques for segmentation of EMG signal are presented: i). Segmentation by identifying the peaks of the MUAPs, ii). by finding the beginning extraction point (BEP) and ending extraction point (EEP) of MUAPs and iii) by using discrete wavelet transform (DWT). A total of 12 EMG signals obtained from 3 normal (NOR) subjects, 5 myopathic (MYO) subjects and 4 motor neuron diseased (MND) subjects were analyzed. The success rate for the technique used peaks to extract MUAPs was 95.90%, for the technique used BEPs and EEPs was 75.39% and for the technique used DWT was 66.64%.