Wavelet Neural Network as EMG classifier

Abstract

This paper presents the use of a Wavelet Neural Network (WNN) as an efficient classifier of Electromyographic (EMG) signals. Generally, an EMG signal requires advanced methods for detection, decomposition, processing and classification. In this paper a WNN model will relate the firing frequency of motor unit action potentials (MUAPs) and three different muscle force levels, in order to improve the classification process showed by other common processing techniques. Adequate EMG classification provides an important source of information in fields such as the diagnosis of neuromuscular disorders, management rehabilitation and prosthesis control were identify and classify MUAPs is a priority task. Accurate and computational efficient EMG classifier was obtained employing a WNN model; the success classification rate was greater than 90% for original registers and 83.33% in adding 50% of noise. WNN allow the feature extraction of EMG signals while creating a classification model, all in a single step, becoming an innovative data processing tool.