In this contribution, a novel technique for discrimination of myopathy, amyotrophic lateral sclerosis (ALS) and healthy electromyograms is proposed using multifractal detrended fluctuation analysis (DFA). Electromyograms are aperiodic and non-stationary electrical signals which represent the complex dynamics of skeletal muscle tissues and nerve cells activities within the human body. In this study, non-linear and dynamic fluctuations of noisy and chaotic electromyograms are analysed using fractal geometry. Electromyography (EMG) signals of myopathy, ALS and healthy disorders were collected from an online existing database and the non-linear dynamics were initially characterised using multifractal DFA. Following this, five novel feature parameters were extracted from the multifractal spectrum of respective EMG signals. Analysis of variance test was conducted on the selected features to examine their statistical significance. Finally, classification of myopathy, ALS and healthy electromyograms was done using support vector machine and k-nearest neighbour classifiers. In this study, four classification tasks are reported and it was observed that the performance of the proposed method is reasonably satisfactory in discriminating different categories of electromyograms. In addition, the proposed method is also found to deliver comparable and even better results in comparison with the existing methods, studied on the same data set.

中文翻译：

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从肌电图的多重分形谱中提取特征以诊断神经肌肉疾病

在这项贡献中，提出了一种使用多重分形趋势波动分析（DFA）来鉴别肌病，肌萎缩性侧索硬化症（ALS）和健康肌电图的新技术。肌电图是非周期性的和非平稳的电信号，代表人体骨骼肌组织和神经细胞活动的复杂动态。在这项研究中，使用分形几何来分析噪声和混沌肌电图的非线性和动态波动。从在线现有数据库中收集肌病，ALS和健康失调的肌电图（EMG）信号，并最初使用多重分形DFA表征非线性动力学。此后，从各个EMG信号的多重分形谱中提取了五个新颖的特征参数。对所选特征进行方差分析以检查其统计显着性。最后，使用支持向量机和k近邻分类器对肌病，ALS和健康肌电图进行分类。在这项研究中，报告了四个分类任务，并且观察到，该方法在区分肌电图的不同类别方面的性能令人满意。此外，与在相同数据集上研究的现有方法相比，所提出的方法也能提供可比甚至更好的结果。报告了四个分类任务，并且观察到该方法在区分肌电图的不同类别方面的性能令人满意。此外，与在相同数据集上研究的现有方法相比，所提出的方法也能提供可比甚至更好的结果。报告了四个分类任务，并且观察到该方法在区分肌电图的不同类别方面的性能令人满意。此外，与在相同数据集上研究的现有方法相比，所提出的方法也能提供可比甚至更好的结果。