Electromyographic (EMG) signals are utilized to analyze the neuromuscular disorders. Machine learning algorithms have been employed as a decision support system to detect neuromuscular disorders. EMG signals contain noise from different sources, such as electrical and electronic instruments and movement artifacts. In this paper, the multiscale principal component analysis (MSPCA) has been used to remove the impulsive noise from the EMG signals. Then, the dual-tree complex wavelet transform (DT-CWT) is utilized for feature extraction, and the rotation forest ensemble classifier is employed for the recognition of EMG signals. In addition, the performance of several classifiers with rotation forest has been studied. An efficient combination of DT-CWT and rotation forest achieved good performance, using tenfold cross validation regarding the total classification accuracy. Results are promising and showed that the rotation forest achieved an accuracy of 99.7% with clinical EMG signals using support vector machine and 96.6% with simulated EMG signals using the artificial neural network (ANN).

中文翻译：

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使用 DT-CWT 和旋转森林集成分类器诊断神经肌肉疾病

肌电图 (EMG) 信号用于分析神经肌肉疾病。机器学习算法已被用作决策支持系统来检测神经肌肉疾病。EMG 信号包含来自不同来源的噪声，例如电气和电子仪器以及运动伪影。在本文中，多尺度主成分分析 (MSPCA) 已被用于去除 EMG 信号中的脉冲噪声。然后，利用双树复小波变换(DT-CWT)进行特征提取，利用旋转森林集成分类器进行肌电信号识别。此外，还研究了几种具有旋转森林的分类器的性能。DT-CWT 和旋转森林的有效组合取得了良好的性能，使用关于总分类准确度的十倍交叉验证。结果是有希望的，并表明旋转森林使用支持向量机对临床 EMG 信号实现了 99.7% 的准确率，使用人工神经网络 (ANN) 对模拟 EMG 信号实现了 96.6% 的准确率。