EMG-Based Characterization of Pathological Tremor Using the Iterated Hilbert Transform
EMG-Based Characterization of Pathological Tremor Using the Iterated Hilbert Transform
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DOI：
10.1109/tbme.2011.2163069
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发表时间：
2011-10-01
影响因子：
4.6
通讯作者：
Farina, Dario
中科院分区：
文献类型：
--
作者：
Dideriksen, Jakob Lund;Gianfelici, Francesco;Farina, Dario
病理性震颤的识别和表征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，应通过有效的检测技术（从运动学或肌肉记录）来估计震颤信号的幅度和相位特征。本文提出了一种基于迭代希尔伯特变换（IHT）的表面肌电图（EMG）信号估计病理性震颤特征的方法。结果表明，IHT 可以对表面 EMG 中的震颤和自主活动成分进行渐进精确建模，并有效解调病理性震颤参数。该方法对最近的震颤产生模型产生的信号以及受病理性震颤患者的实验记录进行了测试。结果表明，该方法能够有效解调震颤幅度（与施加幅度的平均相关性：R-2 = 0.52）、频率（频率估计中的均方根误差：2.6 Hz）、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）。该方法应用于实验数据表明，估计的震颤分量与肢体运动的惯性测量非常相似（四名患者的峰值互相关：0.62 +/- 0.15）。与经验模态分解的性能相比，所提出的方法被证明在无需先验震颤特征的情况下对于震颤表征更加准确。该方法可以用作抑制震颤策略中控制系统的一部分。
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.