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.
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