Statistical Methods and Artificial Neural Networks Techniques in Electromyography

Statistical Methods and Artificial Neural Networks Techniques in Electromyography

Ahmad Taher Azar (Benha University, Egypt) and Valentina E. Balas (University of Arad, Romania)
Copyright: © 2012 |Pages: 9
DOI: 10.4018/ijsda.2012010103
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Abstract

This work represents a comparative study for the activity of the masseter muscle for patients before trial base denture insertion and the activity of the same muscle after trial denture base insertion for both right and left masseter muscles. The study tried to find if there were significant differences in the activity of the masseter muscle before and after patients wearing their trial denture base using two approaches: parametric statistical methods and a Neural Network Classifier. Statistical analysis was performed on three feature vectors extracted from autoregressive (AR) modeling, Discrete Wavelet Transform (WT), and from Wavelet Packet Transform (WP). The least significant difference test and the student t-test have not proved significant differences in the masseter muscle activity before and after wearing denture. However, using the same feature vectors, a neural network classifier has proved that there are significant differences in the masseter muscle activity before and after patients wearing trial denture base.
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1. Introduction

Structural reorganization of the motoer unit, the smallest functional unit of muscle, takes place because of disorders affecting peripheral nerve and muscle. Motor unit morphology can be studied by recording its electrical activity. The procedure is known as clinical electrmyography (EMG). In clinical EMG motor unit action potentials (MUAP's) are recorded using a needle electrode at mild voluntary contraction. The MUAP reflects the electrical activity of a single anatomical motor unit. It represents the compound action potential of those muscle fibers within the recording range of the electrode (Kallenberg & Hermens, 2006). Features of MUAP's extracted in the time domain such as duration, amplitude, and phases proved to be very valuable in differentiating between muscle and nerve diseases with the duration measure being the key parameter used in clinical practice. However, the measurement of the duration parameter is a difficult task depending on the neurophysiologist and/or the computer aided method used. The definitions of widely accepted criteria that will allow the computer-aided measurement of this parameter are still lacking (Calder et al., 2008; Dimitrova & Dimitrov, 2003).

On the other hand, frequency domain features of MUAP's like mean, or median frequency, bandwidth, and quality factor provide additional information in the assessment of neuromuscular disorders and it has recently been shown that the discriminative power of the MUAP mean or median frequency is comparable to the duration measure or spike duration measure (Pfeiffer & Kunze, 1993). However, it will be possible to rely on the evaluation of a spectral parameter only if each power spectrum estimate does not suffer from large mean square error (MSE), otherwise, the estimates may mislead us in our understanding of the physiology.

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