image thumbnail

Fast and Adaptive Multivariate and Multidimensional EMD

version 1.0.2 (2.79 MB) by Mruthun Thirumalaisamy
Simultaneously multivariate and multidimensional EMD


Updated 24 May 2019

View License

The fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) toolbox contains a family of functions aimed at decomposing data sets of 1, 2 and 3 dimensional nature. The algorithm is based on the work presented in M. R. Thirumalaisamy, P. J. Ansell, ‘Fast and Adaptive Empirical Mode Decomposition for Multidimensional, Multivariate Signals’, IEEE Signal Processing Letters, Vol. 25, No. 10, 2018.

System Requirements:
Depending on your data set, RAM requirements can be on the order of dozens of Gigabytes. As a rough estimate, ensure that your computer has atleast 10 times the data set size in RAM. If your dataset is 100MB then your RAM should roughly be 1GB.

The FA-MVEMD toolbox depends on some third party functions to be able to execute. A list of these functions along with their authors are below:
MinimaMaxima3D - (v1.0 Dec 13, 07 , Sam Pichardo)
extrema - (2004, Carlos Adrián Vargas Aguilera)

Main Features:
The different types of functions in the FA-MVEMD toolbox are classified based on their multivariate or multidimensional capabilities. The general naming convention has been:
where x represents the dimensionality and y represents the number of channels that can be handled by the code. For example, the two-dimensional trivariate code is called by the function EMD2D3V.

The list of available functions are:
EMD1DNV (3-16 channels)


EMD3D3V_parallel (parallelised)
EMD3D3V_parallel_var (parallelised with variable window size in each dimension)

Window Size Types:
The reference document for the definitions of window sizes is 'Fast and Adaptive Bidimensional Empirical Mode Decomposition Using Order-Statistics Filter Based Envelope Estimation, EURASIP Journal on Advances in Signal Processing, Bhuiyan et al., 2008'. In addition to the four types in the article, FA-MVEMD provides three additional filter size choices:

Type 5 - Average of Types 1-4 found in reference
Type 6 - Median extrema distance
Type 7 - Mode extrema distance

Cite As

Mruthun Thirumalaisamy (2021). Fast and Adaptive Multivariate and Multidimensional EMD (, MATLAB Central File Exchange. Retrieved .

Thirumalaisamy, Mruthun R., and Phillip J. Ansell. “Fast and Adaptive Empirical Mode Decomposition for Multidimensional, Multivariate Signals.” IEEE Signal Processing Letters, vol. 25, no. 10, Institute of Electrical and Electronics Engineers (IEEE), Oct. 2018, pp. 1550–54, doi:10.1109/lsp.2018.2867335.

View more styles
MATLAB Release Compatibility
Created with R2019a
Compatible with R2018a and later releases
Platform Compatibility
Windows macOS Linux
Tags Add Tags

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!