Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders

作     者：Istenic, Rok Kaplanis, Prodromos A. Pattichis, Constantinos S. Zazula, Damjan 

作者机构：Univ Maribor Fac Elect Engn & Comp Sci SI-2000 Maribor Slovenia Cyprus Inst Neurol & Genet CY-1683 Nicosia Cyprus Univ Cyprus Dept Comp Sci CY-1678 Nicosia Cyprus 

出 版 物：《MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING》 (医学和生物工程与计算)

年 卷 期：2010年第48卷第8期

页      面：773-781页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 1001[医学-基础医学(可授医学、理学学位)] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：Slovenian Ministry of Higher Education, Science and Technology [1000-05-310083, P2-0041] DePaSSE 

主　　题：Surface electromyogram Neuromuscular disorders Continuous wavelet transform Entropy Support vector classification 

摘      要：We introduce a novel method for an automatic classification of subjects to those with or without neuromuscular disorders. This method is based on multiscale entropy of recorded surface electromyograms (sEMGs) and support vector classification. The method was evaluated on a single-channel experimental sEMGs recorded from biceps brachii muscle of nine healthy subjects, nine subjects with muscular and nine subjects with neuronal disorders, at 10%, 30%, 50%, 70% and 100% of maximal voluntary contraction force. Leave-one-out cross-validation was performed, deploying binary (healthy/patient) and three-class classification (healthy/myopathic/neuropathic). In the case of binary classification, subjects were distinguished with 81.5% accuracy (77.8% sensitivity at 83.3% specificity). At three-class classification, the accuracy decreased to 70.4% (myopathies were recognized with a sensitivity of 55.6% at specificity 88.9%, neuropathies with a sensitivity of 66.7% at specificity 83.3%). The proposed method is suitable for fast and non-invasive discrimination of healthy and neuromuscular patient groups, but it fails to recognize the type of pathology.