Although a well-established body of literature has examined electrophysiological muscle classification methods and systems, ways to enhance their transparency is still an important challenge and requires further study. In this work, a transparent semi-supervised electrophysiological muscle classification system which uses needle-detected EMG signals to classify muscles as normal, myopathic, or neurogenic is proposed. The electrophysiological muscle classification (EMC) problem is naturally formulated using multiple instance learning (MIL) and needs an adaptation of standard supervised classifiers for the purpose of training and evaluating bags of instances. Here, a novel MIL-based EMC system in which the muscle classifier uses predictions based on motor unit potentials (MUPs) to infer muscle labels is described. This system uses morphological, stability, near fiber and spectral MUP features. Quantitative results obtained from applying the proposed transparent system to four electrophysiologically different groups of muscles, composed of proximal and distal hand and leg muscles, resulted in an average classification accuracy of 95.85%. The findings show the superior and stable performance of the proposed EMC system compared to previous works using other supervised, semi-supervised and unsupervised methods.

中文翻译：

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基于模糊的多实例学习从肌电信号的透明电生理肌肉分类

尽管成熟的文献研究了电生理学肌肉分类方法和系统，但是提高其透明度的方法仍然是一个重要的挑战，需要进一步研究。在这项工作中，提出了一种透明的半监督电生理肌肉分类系统，该系统使用针头检测到的EMG信号将肌肉分类为正常，肌病或神经性。电生理肌肉分类（EMC）问题是使用多实例学习（MIL）自然制定的，并且需要对标准监督分类器进行修改，以训练和评估实例包。这里，描述了一种新颖的基于MIL的EMC系统，其中，肌肉分类器使用基于运动单位电位（MUP）的预测来推断肌肉标签。该系统使用形态学，稳定性，近光纤和频谱MUP功能。通过将拟议的透明系统应用到由近端和远端的手和腿部肌肉组成的四个电生理学不同的肌肉组获得的定量结果，得出的平均分类准确率为95.85％。研究结果表明，与以前使用其他有监督，半监督和无监督方法的工作相比，拟议的EMC系统具有优越且稳定的性能。