English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Empirical mode decomposition (EMD) is a signal processing technique proposed for the analysis of non-stationary and nonlinear signals [1]. EMD has been successfully applied to solve numerous practical problems in various applications [2-9]. This technique decomposes a time series into a set of zero-mean underlying components called intrinsic mode functions (IMF). The main advantage of EMD is that it is an adaptive method. For example, the EMD algorithm depends only on the signal under analysis and does not require any a priori defined basis system. One of the main drawbacks of EMD is mode mixing that occurs when either signal of a similar scale resides in more than one IMF or an IMF consists of signals of broadly different scales [10]. This issue may cause some IMFs to become physically meaningless. Ensemble EMD (EEMD) was developed to overcome the EMD mode mixing issue [10]. The improved algorithm, EEMD, is based on one of the most important properties of EMD, namely that EMD behaves as a dyadic filter bank when applied to white Gaussian noise [11,12]. The principle of EEMD is to add a finite number of white noise series to the signal of interest. These background white noise series provide a time-frequency reference frame for the original signal. The filter bank properties of EMD help the signal components to be projected on the proper scales of this reference frame. Since the white noise series are different in each trial, the noise cancels out for a sufficiently large number of ensembles, leaving only the persistent part of the signals. As a result, the components of similar scales are expected to reside in the same IMFs which reduce the mode mixing problem [10].

Noise Cancellation from Vibrocardiographic Signals Based on the Ensemble Empirical Mode Decomposition