Sliding Window Empirical Mode Decomposition -its performance and quality
Nalecz Institute of Biocybernetics and Biomedical Engineering PAS, Warsaw, Poland
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Accepted: 11 September 2014
Published online: 22 November 2014
In analysis of nonstationary nonlinear signals the classical notion of frequency is meaningless. Instead one may use Instantaneous Frequency (IF) that can be interpreted as the frequency of a sine wave which locally fits the signal. IF is meaningful for monocomponent nonstationary signals and may be calculated by Hilbert transform (HT).
A multicomponent signal may be decomposed into its monocomponents. Empirical Mode Decomposition (EMD), developed by Norden E. Huang, is a new method of such breaking down of a signal into its monocomponents. EMD combined with HT (called Hilbert-Huang Transform) is a good tool for analyzing nonstationary signals, but unfortunately the traditional EMD algorithm consumes a lot of time and computer resources. I propose a modified EMD algorithm - Sliding Window EMD, SWEMD.
Proposed algorithm speeds up (about 10 times) the computation with acceptable quality of decomposition.
Sliding Window EMD algorithm is suitable for decomposition of long signals with high sampling frequency.
Key words: Empirical Mode Decomposition / EMD / Fast EMD / Sliding Window Empirical Mode Decomposition / SWEMD / Signal analysis
© The Author(s), 2014