@article {10.3844/amjnsp.2019.8.12,
article_type = {journal},
title = {Determination of Neuromuscular Diseases Using Complexity of Electromyogram Signals},
author = {Ahammed, Kawser and Ahmed, Mosabber Uddin},
volume = {10},
year = {2019},
month = {Dec},
pages = {8-12},
doi = {10.3844/amjnsp.2019.8.12},
url = {https://thescipub.com/abstract/amjnsp.2019.8.12},
abstract = {Characterization of Electromyogram (EMG) signals is important for identifying neuromuscular diseases. Although various techniques were implemented for classification, none of them were implemented in the complexity domain. In this study, characterization of EMG signals recorded from healthy, neuropathic and myopathic subjects has been performed in complexity domain based on Multiscale Entropy (MSE) method. To do this, the multiscale entropy method has been applied to an EMG database publicly available in PhysioNet. The complexity profile curves obtained with the MSE approach have shown promising classification among healthy subject, neuropathic and myopathic patients in terms of complexity. One way ANOVA test has shown statistically significant differences (p},
journal = {Neuroscience International},
publisher = {Science Publications}
}