Sezgin Necmettin
Department of Electrical and Electronics Engineering, Faculty of Architecture and Engineering, Batman University, 72060 Batman, Turkey.
ScientificWorldJournal. 2012;2012:478952. doi: 10.1100/2012/478952. Epub 2012 Oct 24.
The analysis and classification of electromyography (EMG) signals are very important in order to detect some symptoms of diseases, prosthetic arm/leg control, and so on. In this study, an EMG signal was analyzed using bispectrum, which belongs to a family of higher-order spectra. An EMG signal is the electrical potential difference of muscle cells. The EMG signals used in the present study are aggressive or normal actions. The EMG dataset was obtained from the machine learning repository. First, the aggressive and normal EMG activities were analyzed using bispectrum and the quadratic phase coupling of each EMG episode was determined. Next, the features of the analyzed EMG signals were fed into learning machines to separate the aggressive and normal actions. The best classification result was 99.75%, which is sufficient to significantly classify the aggressive and normal actions.
为了检测疾病的某些症状、控制假肢等，肌电图（EMG）信号的分析和分类非常重要。在本研究中，使用属于高阶谱族的双谱对肌电信号进行了分析。肌电信号是肌肉细胞的电位差。本研究中使用的肌电信号是攻击性动作或正常动作。肌电数据集来自机器学习库。首先，使用双谱分析攻击性和正常的肌电活动，并确定每个肌电片段的二次相位耦合。接下来，将分析后的肌电信号特征输入学习机器，以区分攻击性动作和正常动作。最佳分类结果为99.75%，足以显著区分攻击性动作和正常动作。
