Nagineni Sukumar, Taran Sachin, Bajaj Varun
PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, 452005 India.
Health Inf Sci Syst. 2018 Sep 20;6(1):13. doi: 10.1007/s13755-018-0050-4. eCollection 2018 Dec.
Neuromuscular disorder is a muscular and nervous disorder resulting in muscular weakness and progressively damages nervous control, such as amyotrophic lateral sclerosis (ALS) and myopathy (MYO). Its diagnosis can be possible by classification of ALS, MYO, and normal electromyogram (EMG) signals. In this paper, an effective method based on variational mode decomposition (VMD) is proposed for identification of neuromuscular disorder of EMG signals. VMD is an adaptive signal decomposition which decomposes EMG signals nonrecursively into band-limited functions or modes. These modes are used for extraction of spectral features, particularly spectral flatness, spectral spread, spectral decrease and statistical features like kurtosis, mean absolute deviation, and interquartile range. The extracted features are fed to the extreme learning machine classifier in order to classify neuromuscular disorder of EMG signals. The performance of obtained results shows that the method used provides a better classification for neuromuscular disorder of EMG signals as compared to existing methods.
神经肌肉疾病是一种肌肉和神经紊乱疾病，会导致肌肉无力，并逐渐损害神经控制，如肌萎缩侧索硬化症（ALS）和肌病（MYO）。通过对ALS、MYO和正常肌电图（EMG）信号进行分类，可以实现对其诊断。本文提出了一种基于变分模态分解（VMD）的有效方法，用于识别EMG信号的神经肌肉疾病。VMD是一种自适应信号分解方法，它将EMG信号非递归地分解为带限函数或模态。这些模态用于提取频谱特征，特别是频谱平坦度、频谱扩展、频谱衰减以及诸如峰度、平均绝对偏差和四分位间距等统计特征。提取的特征被输入到极限学习机分类器中，以对EMG信号的神经肌肉疾病进行分类。所得结果的性能表明，与现有方法相比，所采用的方法为EMG信号的神经肌肉疾病提供了更好的分类。
