Lambert G, Beuter A, MacGibbon B
Département de mathématiques, UQAM, Montréal, Canada.
Brain Cogn. 2000 Jun-Aug;43(1-3):296-301.
The discrimination between normal and pathological tremor is difficult when amplitude is relatively small. The WFLC algorithm, a time domain adaptive Fourier transform, is designed to control physiological tremor and to improve precision during microsurgery. We added two iterative optimization processes to initialize the following parameters: initial frequency weight (omega0), amplitude adaptation rate (mu) and frequency adaptation rate (mu0). Then, we applied the methods on data sets recorded on patients with different tremors (control, parkinsonian, cerebellar, and essential) sampled at 200 Hz. After filtering the data, the WFLC algorithm tracked the time-varying dominant frequencies and amplitudes of the transformed data sets. Our results illustrate the potential of using this algorithm as an approach to discriminate between normal and pathological signals even when amplitude is not a significant discriminating factor.
当振幅相对较小时，区分正常震颤和病理性震颤很困难。WFLC算法是一种时域自适应傅里叶变换，旨在控制生理性震颤并提高显微手术期间的精度。我们添加了两个迭代优化过程来初始化以下参数：初始频率权重（omega0）、振幅适应率（mu）和频率适应率（mu0）。然后，我们将这些方法应用于以200Hz采样的不同震颤患者（对照、帕金森氏症、小脑和特发性）记录的数据集。对数据进行滤波后，WFLC算法跟踪变换后数据集的时变主导频率和振幅。我们的结果表明，即使振幅不是一个重要的区分因素，使用该算法作为区分正常信号和病理信号的方法也具有潜力。
