Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, SI-2000, Maribor, Slovenia.
Med Biol Eng Comput. 2010 Aug;48(8):773-81. doi: 10.1007/s11517-010-0629-7. Epub 2010 May 21.
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.
我们介绍了一种新的方法，用于自动对患有神经肌肉疾病或无神经肌肉疾病的患者进行分类。该方法基于记录的表面肌电图（sEMG）的多尺度熵和支持向量分类。该方法在 9 名健康受试者、9 名肌肉疾病患者和 9 名神经疾病患者的肱二头肌单通道实验性 sEMG 记录上进行了评估，记录的最大随意收缩力分别为 10%、30%、50%、70%和 100%。采用 10 折交叉验证，进行二分类（健康/患者）和三分类（健康/肌病/神经病）。在二分类的情况下，受试者的准确率为 81.5%（特异性为 83.3%时的敏感度为 77.8%）。在三分类的情况下，准确率下降至 70.4%（肌病的敏感度为 55.6%，特异性为 88.9%，神经病的敏感度为 66.7%，特异性为 83.3%）。所提出的方法适用于快速和非侵入性地区分健康和神经肌肉疾病患者群体，但无法识别病理类型。
