Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders
Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders
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DOI：
10.1007/s10916-014-0031-3
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发表时间：
2014-04-01
影响因子：
5.3
通讯作者：
Subasi, Abdulhamit
中科院分区：
文献类型：
--
作者：
Gokgoz, Ercan;Subasi, Abdulhamit
不同的方法已被应用于肌电信号的定量分析。介绍了多尺度主成分分析（MSPCA）去噪方法在肌电信号分类中的应用。讨论了MSPCA去噪方法对肌电信号分类的影响。此外，还比较了MUSIC （Multiple Single Classification）特征提取方法对肌电信号的分类效果。结果是根据肌电信号数据进行正常、肌萎缩侧索硬化症和肌病的分类。此外，还讨论了k-最近邻（k-NN）、人工神经网络（ANN）和支持向量机（svm）等分类器的总准确率。采用MSPCA去噪方法得到了显著的结果。所开发的分类器之间的比较是基于一些标量性能，如灵敏度，特异性，准确性，F-measure和ROC曲线下面积（AUC）。结果表明，与未进行MSPCA去噪的肌电图数据相比，MSPCA去噪的准确性有了显著提高。
Different approaches have been applied for quantitative analysis of EMG signals. This study introduces the effect of Multiscale Principal Component Analysis (MSPCA) denoising method in ElectroMyoGram (EMG) signal classification. The effect of the MSPCA denoising method discussed on EMG signal classification. In addition, effect of Multiple Single Classification (MUSIC) feature extraction method presented and compared for the classification of EMG signals. The results were accomplished on the basis of EMG signal data to classify into normal, ALS or myopathic. Furthermore, total accuracy of classifiers such as k-Nearest Neighbor (k-NN), Artificial Neural Network (ANN) and Support Vector Machines (SVMs) were discussed. Significant results were found by using MSPCA denoising method. The comparisons between the developed classifiers were based on a number of scalar performances such as sensitivity, specificity, accuracy, F-measure and area under ROC curve (AUC). The results show that MSPCA de-noising has considerably increased the accuracy as compared to EMG data without MSPCA de-noising.