Single-Channel EMG Classification With Ensemble-Empirical-Mode-Decomposition-Based ICA for Diagnosing Neuromuscular Disorders
Single-Channel EMG Classification With Ensemble-Empirical-Mode-Decomposition-Based ICA for Diagnosing Neuromuscular Disorders
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DOI：
10.1109/tnsre.2015.2454503
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发表时间：
2016-07-01
影响因子：
4.9
通讯作者：
Nguyen, Hung T.
中科院分区：
文献类型：
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作者：
Naik, Ganesh R.;Selvan, S. Easter;Nguyen, Hung T.
肌电图（EMG）信号的准确和计算效率高的定量分析在神经肌肉疾病的诊断，假肢和一些相关的应用中起着不可避免的作用。由于通常情况下，测量的信号是从周围肌肉（源）发出的电势的混合物，因此许多EMG信号处理方法依赖于线性源分离技术，例如独立分量分析（伊卡）。然而，伊卡算法的幼稚实现不符合从单通道EMG测量中提取潜在源的任务。在这方面，目前的工作集中在神经肌肉疾病的分类方法，处理使用单通道EMG传感器记录的数据。集成经验模态分解算法将单通道肌电信号分解为一组噪声消除的固有模态函数，这些固有模态函数又由FastICA算法分离。从分离的分量中提取的五个时域特征的缩减集使用线性判别分析进行分类，并且分类结果用多数表决方案进行微调。所提出的方法的性能已被验证与临床肌电图数据库，报告了较高的分类准确率（98%）。这项研究的结果鼓励这种方法可能的扩展到真实的设置，以帮助临床医生作出正确的诊断神经肌肉疾病。
An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Since it is often the case that the measured signals are the mixtures of electric potentials that emanate from surrounding muscles (sources), many EMG signal processing approaches rely on linear source separation techniques such as the independent component analysis (ICA). Nevertheless, naive implementations of ICA algorithms do not comply with the task of extracting the underlying sources from a single-channel EMG measurement. In this respect, the present work focuses on a classification method for neuromuscular disorders that deals with the data recorded using a single-channel EMG sensor. The ensemble empirical mode decomposition algorithm decomposes the single-channel EMG signal into a set of noise-canceled intrinsic mode functions, which in turn are separated by the FastICA algorithm. A reduced set of five time domain features extracted from the separated components are classified using the linear discriminant analysis, and the classification results are fine-tuned with a majority voting scheme. The performance of the proposed method has been validated with a clinical EMG database, which reports a higher classification accuracy (98%). The outcome of this study encourages possible extension of this approach to real settings to assist the clinicians in making correct diagnosis of neuromuscular disorders.