Using Empirical Mode Decomposition Weighting Functionals as Time Series Filtering Coefficients for Physiological Recordings

We present a method for enhancing signals possessing nonlinear and nonstationary characteristics, which we call weighting functional-empirical mode decomposition (WF-EMD). The filtering method is based upon the empirical mode decomposition (EMD) and utilizes an energy-based weighting scheme to recombine the decomposed modes into a single cleansed version of the signal.

The filter has been developed in such a way that no restrictive assumptions about the data are required. Furthermore, the temporal resolution of the data is left unaltered, as it would occur in many common data-smoothing methods. The design of this filter has been influenced by improving the calculation accuracy of dynamical measures, such as fractal dimensions and Lyapunov exponents, of neurodynamical recordings such as those obtained through electroencephalography (EEG) or magnetoencephalography (MEG).

The article was published in: Advances in Adaptive Data Analysis 4(01n02): 1250015.

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This work was supported (in part) by the Fetzer Franklin Fund of the John E. Fetzer Memorial Trust.