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

Intramuscular electromyographic (EMG) signals are detected with needles or wires inserted
into muscles. With respect to non-invasive techniques, intramuscular electromyography has …

Muscle activity decreases due to various conditions like age factors and muscle diseases
namely, amyotrophic lateral sclerosis (ALS) and myopathy. Electromyogram (EMG) signals …

The neuromuscular disorders are diagnosed using electromyographic (EMG) signals.
Machine learning algorithms are employed as a decision support system to diagnose …

An accurate and computationally efficient quantitative analysis of electromyography (EMG)
signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and …

The rapid advancements in artificial intelligence (AI), including machine learning (ML), and
deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare …

A new approach has been developed by combining the K-mean clustering (KMC) method
and a modified convolution kernel compensation (CKC) method for multichannel surface …

The motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide a
significant source of information for the assessment of neuromuscular disorders. In this work …

This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG)
signal stream, during the golf swing using a 7-iron club and exploits information extracted …

In this paper, two schemes for neuromuscular disease classification from electromyography
(EMG) signals are proposed based on discrete wavelet transform (DWT) features. In the first …