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.

中文翻译：

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一种用于时间序列分析的复杂网络方法在神经肌肉疾病诊断中的应用

肌电图 (EMG) 是指指示神经肌肉活动和肌肉形态的生物医学信号。专家使用此时间序列准确诊断神经肌肉疾病。现代数据分析技术最近引入了将时间序列数据映射到图形和复杂网络的新方法，这些方法在包括医学在内的不同领域都有应用。由此产生的网络开发出一种完全不同的视力，可用于补充医生对时间序列的发现。这可以导致更丰富的分析、减少的错误、更准确的疾病诊断以及提高治疗过程的准确性和速度。映射过程可能会导致时间序列中重要数据的丢失，并且无法保留所有时间序列特征。因此，实现一种能够很好地表示时间序列同时保持基本特征的方法是至关重要的。本文提出了一种名为 GraphTS 的网络开发新方法，以通过使用可见性图方法的 EMG 时间序列克服现有方法的有限精度。为此，EMG 信号被预处理并通过标准可见性图算法映射到复杂网络。由此产生的网络可以区分健康样本和患者样本。在下一步中，在提取最优特征后，以特征矩阵的形式给出所开发网络的属性作为分类器的输入。所提出的深度神经网络方法的性能评估显示，训练数据的准确率为 99.30%，测试数据的准确率为 99.18%。所以，