  Electromyography (EMG) refers to a biomedical signal indicating neuromuscular
activity and muscle morphology. Experts accurately diagnose neuromuscular
disorders using this time series. Modern data analysis techniques have recently
led to introducing novel approaches for mapping time series data to graphs and
complex networks with applications in diverse fields, including medicine. The
resulting networks develop a completely different visual acuity that can be
used to complement physician findings of time series. This can lead to a more
enriched analysis, reduced error, more accurate diagnosis of the disease, and
increased accuracy and speed of the treatment process. The mapping process may
cause the loss of essential data from the time series and not retain all the
time series features. As a result, achieving an approach that can provide a
good representation of the time series while maintaining essential features is
crucial. This paper proposes a new approach to network development named
GraphTS to overcome the limited accuracy of existing methods through EMG time
series using the visibility graph method. For this purpose, EMG signals are
pre-processed and mapped to a complex network by a standard visibility graph
algorithm. The resulting networks can differentiate between healthy and patient
samples. In the next step, the properties of the developed networks are given
in the form of a feature matrix as input to classifiers after extracting
optimal features. Performance evaluation of the proposed approach with deep
neural network shows 99.30% accuracy for training data and 99.18% for test
data. Therefore, in addition to enriched network representation and covering
the features of time series for healthy, myopathy, and neuropathy EMG, the
proposed technique improves accuracy, precision, recall, and F-score.
