Electromyography (EMG) is a biomedical signal that shows information about the neuromuscular activity as well as muscular morphology. This signal is used to diagnose neuromuscular disorders. Improved analysis of electromyographic data will help the experts to correctly diagnose neuromuscular disorders, seizures, and related diseases, and to accelerate the healing process. Recently, several techniques are introduced fro mapping from the time series to the complex network. The time series is analyzed with the characteristics of a complex network. These methods are used in many scientific and research fields and could be exploited in understanding the dynamics or predicting how the system evolves.The resulting networks create a completely different visual that can be used by the physician to complement what is being taken from EMG signals. As a result, medical errors are reduced and the treatment process is carried out more accurately and quickly, following the correct identification of the disease and examining its various dimensions. The characteristics of a time series are mapped to summarized criteria. This summarization might lead to missing important information and prevent the model from preserving all the properties of a time series. Therefore, it is still challenging to find an approach that can maintain all the features of the time series and have a good representation of it. In this study, a new approach to building a network from an electromyographic time series is proposed using the visibility graph algorithm. The proposed method fills the shortcoming of previous approaches in terms of insufficient accuracy in terms of maintaining all the features of the time series and low rgb(29, 34, 40); font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 13px; text-align: left; text-indent: 0px; white-space: normal;" Electromyography, rgb(29, 34, 40); font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 13px; text-align: left; text-indent: 0px; white-space: normal;"
