Department of Health and Science Technology, Faculty of Engineering, Medicine, and Sport Science, Aalborg University, DK-9220 Aalborg, Denmark.
IEEE Trans Biomed Eng. 2011 Oct;58(10):2911-21. doi: 10.1109/TBME.2011.2163069. Epub 2011 Jul 29.
The identification and characterization of pathological tremor are necessary for the development of techniques for tremor suppression, for example, based on functional electrical stimulation. For this purpose, the amplitude and phase characteristics of the tremor signal should be estimated by effective detection techniques, either from the kinematics or from muscle recordings. This paper presents an approach for the estimation of the characteristics of pathological tremor from the surface electromyogram (EMG) signal based on the iterated Hilbert transform (IHT). It is shown that the IHT allows an asymptotically exact modeling of the tremor and the voluntary activity components in the surface EMG, and an effective demodulation of the pathological tremor parameters. The method was tested on signals generated by a recent model for tremor generation as well as experimentally recorded from patients affected by pathological tremor. The results showed the ability of the proposed approach to demodulate effectively the tremor amplitude (average correlation with imposed amplitude: R(2)=0.52), the frequency (root mean square error in frequency estimation: 2.6 Hz), and phase, as well as the degree of voluntary activity (correlation with simulated inertial load: R(2)=0.62). The application of the method to the experimental data indicated that the estimated tremor component closely resembles inertial measurements of limb movement (peak cross correlation across four patients: 0.62±0.15). Compared to the performance of empirical mode decomposition, the proposed method proved to be more accurate for tremor characterization without a priori knowledge of the tremor characteristics. This method can be used as a part of a control system in strategies for suppression of tremor.
病理性震颤的识别和特征分析对于开发震颤抑制技术（例如基于功能性电刺激的技术）是必要的。为此，应该通过有效的检测技术来估计震颤信号的幅度和相位特征，无论是从运动学还是从肌肉记录中。本文提出了一种基于迭代希尔伯特变换（IHT）从表面肌电图（EMG）信号估计病理性震颤特征的方法。结果表明，IHT 允许对表面 EMG 中的震颤和自主活动成分进行渐近精确的建模，并有效地解调病理性震颤参数。该方法在最近的震颤生成模型生成的信号以及从受病理性震颤影响的患者记录的实验信号上进行了测试。结果表明，所提出的方法能够有效地解调震颤幅度（与施加幅度的平均相关性：R(2)=0.52）、频率（频率估计的均方根误差：2.6 Hz）和相位，以及自主活动程度（与模拟惯性负载的相关性：R(2)=0.62）。该方法在实验数据上的应用表明，估计的震颤分量与肢体运动的惯性测量非常相似（四个患者的峰值互相关：0.62±0.15）。与经验模态分解的性能相比，该方法在没有先验震颤特征知识的情况下，更准确地用于震颤特征分析。该方法可以用作抑制震颤策略中的控制系统的一部分。
