Effective electromyographic (EMG) signal characterization is critical in the diagnosis of
neuromuscular disorders. Machine-learning based pattern classification algorithms are …

Abstract The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest
among researches and practitioners in a wide variety of fields. The SOM has been analyzed …

The need to increase machine reliability and decrease production loss due to faulty products
in highly automated line requires accurate and reliable fault classification technique …

Goal: This article presents the design and validation of an accurate automatic diagnostic
system to classify intramuscular EMG (iEMG) signals into healthy, myopathy, or neuropathy …

Wavelets are a powerful tool for biomedical signal processing. Wavelets are used for the
processing of signals that are non-stationary and time varying. The EMG signal contains …

According to the World Federation of Deaf [1], there are about 70 million deaf people who
use sign language as their first language. Despite the fact that sign languages represent the …

In this paper, we compare the performances of classifier combination methods (bagging,
modified random subspace method, classifier selection, parametric fusion) to logistic …

Automated diagnosis of neuromuscular disorders such as myopathy and neuropathy can be
done by measuring and analyzing the nonlinear and non-stationary trends in …

Although a well-established body of literature has examined electrophysiological muscle
classification methods and systems, ways to enhance their transparency is still an important …

Electrophysiological muscle classification involves characterization of extracted motor unit
potentials (MUPs) followed by the aggregation of these MUP characterizations. Existing …