Performance evaluation of ensemble empirical mode decomposition

Journal article

Empirical mode decomposition (EMD) is an adaptive, data-driven algorithm that decomposes any time series into its intrinsic modes of oscillation, which can then be used in the calculation of the instantaneous phase and frequency. Ensemble EMD (EEMD), where the final EMD is estimated by averaging numerous EMD runs with the addition of noise, was an advancement introduced by Wu and Huang (2008) to try increasing the robustness of EMD and alleviate some of the common problems of EMD such as mode mixing. In this work, we test the performance of EEMD as opposed to normal EMD, with emphasis on the effect of selecting different stopping criteria and noise levels. Our results indicate that EEMD, in addition to slightly increasing the accuracy of the EMD output, substantially increases the robustness of the results and the confidence in the decomposition. © 2009 World Scientific Publishing Company.

Authors: Niazy, R; Beckmann, C; Brady, J; Smith, S

• Journal: Advances in Adaptive Data Analysis
• Volume: 1
• Issue: 2
• Pages: 231-242
• Publication date: 2009-04-01
• DOI: 10.1142/S1793536909000102
• EISSN: 1793-7175
• ISSN: 1793-5369
• Language: English
• Keywords: Hilbert-Huang transform; stopping criteria; Ensemble empirical mode decomposition