Type: Package
Title: Variational Mode Decomposition
Version: 1.0.1
Date: 2022-07-04
Maintainer: Lampros Mouselimis <mouselimislampros@gmail.com>
BugReports: https://github.com/mlampros/VMDecomp/issues
URL: https://github.com/mlampros/VMDecomp
Description: 'RcppArmadillo' implementation for the Matlab code of the 'Variational Mode Decomposition' and 'Two-Dimensional Variational Mode Decomposition'. For more information, see (i) 'Variational Mode Decomposition' by K. Dragomiretskiy and D. Zosso in IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531-544, Feb.1, 2014, <doi:10.1109/TSP.2013.2288675>; (ii) 'Two-Dimensional Variational Mode Decomposition' by Dragomiretskiy, K., Zosso, D. (2015), In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, <doi:10.1007/978-3-319-14612-6_15>.
License: GPL-3
Encoding: UTF-8
Copyright: inst/COPYRIGHTS
SystemRequirements: libarmadillo: apt-get install -y libarmadillo-dev (deb), libblas: apt-get install -y libblas-dev (deb), liblapack: apt-get install -y liblapack-dev (deb), libarpack++2: apt-get install -y libarpack++2-dev (deb), gfortran: apt-get install -y gfortran (deb)
Depends: R(≥ 3.5.0)
Imports: Rcpp (≥ 1.0.8.3), data.table, glue
LinkingTo: Rcpp, RcppArmadillo
Suggests: OpenImageR, R.matlab, testthat (≥ 3.0.0), rmarkdown, knitr
RoxygenNote: 7.1.2
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2022-07-04 14:49:37 UTC; lampros
Author: Lampros Mouselimis ORCID iD [aut, cre], Dominique Zosso [cph] (Author of the Variational Mode Decomposition Matlab code), Konstantin Dragomiretskiy [cph] (Author of the Variational Mode Decomposition Matlab code)
Repository: CRAN
Date/Publication: 2022-07-04 15:40:02 UTC

Sample Arrhythmia Data from MIT Boston's Beth Israel Hospital (BIH) Database

Description

Sample arrhythmia data from the MIT-BIH Arrhythmia Database

Usage

data(arrhythmia)

Format

An object of class data.table (inherits from data.frame) with 10000 rows and 2 columns.

Details

The data includes two columns "MLII" and "V1". According to https://www.physionet.org/files/mitdb/1.0.0/mitdbdir/intro.htm, "In most records, the upper signal is a modified limb lead II (MLII), obtained by placing the electrodes on the chest. The lower signal is usually a modified lead V1 (occasionally V2 or V5, and in one instance V4)."

The data was downloaded after installing the "wfdb" Python package. The Python code used to save the sample data is the following:

import wfdb
import pandas as pd

sample_annotat_200 = wfdb.rdrecord('200', sampfrom = 0, sampto = 10000, pn_dir = 'mitdb')
arrhythmia = pd.DataFrame(sample_annotat_200.p_signal, columns = sample_annotat_200.sig_name)

References

Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001). (PMID: 11446209)

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation (Online). 101 (23), pp. e215–e220.

https://physionet.org/content/mitdb/1.0.0/

https://www.physionet.org/files/mitdb/1.0.0/mitdbdir/intro.htm

https://github.com/MIT-LCP/wfdb-python

https://github.com/MIT-LCP/wfdb-python/blob/main/demo.ipynb

Examples


require(VMDecomp)

data(arrhythmia)

elapsed time in hours & minutes & seconds

Description

elapsed time in hours & minutes & seconds

Usage

compute_elapsed_time(time_start)

Arguments

time_start

a numeric value specifying the start time

Value

It does not return a value but only prints the time in form of a character string in the R session

Estimation of Intrinsic Mode Function (IMF) Number in Variational Mode Decomposition

Description

Estimation of Intrinsic Mode Function (IMF) Number in Variational Mode Decomposition

Usage

estimate_k_modes(
  signal_1d,
  cor_thresh,
  default_vmd_params,
  min_K = 2,
  seed = 1,
  verbose = FALSE
)

Arguments

signal_1d

a numeric vector specifying the 1-dimensional input signal

cor_thresh

a numeric value specifying the minimum (positive or negative) correlation coefficient threshold where decomposition will be stopped (a value between 0.0 and 1.0)

default_vmd_params

a list of parameters consisting of the (remaining) Variational Mode Decomposition default parameters (except for 'data' and 'K')

min_K

a numeric value specifying the minimum value of the K (modes) parameter (from which decomposition starts)

seed

a numeric value specifying the seed (for reproducibility purposes)

verbose

a boolean. If TRUE then information will be printed in the console

Details

Correlation Coefficient Method:

Value

a numeric value specifying the optimal K parameter

References

https://doi.org/10.1155/2020/8304903

Examples


## Not run: 

require(VMDecomp)
data(arrhythmia)

default_vmd_params = list(alpha = 2000,
                          tau = 0,
                          DC = FALSE,
                          init = 1,
                          tol = 1e-6)

res_k = estimate_k_modes(signal_1d = arrhythmia[['MLII']],
                         cor_thresh = 0.1,
                         default_vmd_params = default_vmd_params,
                         min_K = 2,
                         seed = 1,
                         verbose = TRUE)
res_k

## End(Not run)

inner function of 'compute_elapsed_time'

Description

inner function of 'compute_elapsed_time'

Usage

inner_elapsed_time(secs, estimated = FALSE)

Arguments

secs

a numeric value specifying the seconds

estimated

a boolean. If TRUE then the output label becomes the 'Estimated time'

Value

a character string showing the estimated or elapsed time

Variational Mode Decomposition (1- or 2-dimensional)

Description

Variational Mode Decomposition (1- or 2-dimensional)

Usage

vmd(data, alpha, tau, K, DC, init, tol, verbose = FALSE)

Arguments

data

either a vector or a matrix (of type numeric or integer)

alpha

a numeric value specifying the balancing parameter of the data-fidelity constraint

tau

a numeric value specifying the time-step of the dual ascent ( pick 0 for noise-slack )

K

a numeric value specifying the number of modes to be recovered

DC

a boolean. If true the first mode is put and kept at DC (0-freq)

init

a numeric value. This parameter differs depending on the input 'data' parameter (1-dimensional and 2-dimensional). See the details section for more information

tol

a numeric value specifying the tolerance of convergence criterion (typically this parameter is around 1e-6 for the 1-dimensional and 1e-7 for the 2-dimensional data)

verbose

a boolean. If TRUE then information will be printed in the console

Details

The 'init' parameter takes the following values for,

Value

a list object of length three which includes the

References

https://math.montana.edu/dzosso/code/

Examples


require(VMDecomp)

#..............
# 1-dimensional
#..............

N = 250

set.seed(1)
rand_unif = runif(n = N, min = 0, max = 1.0)

f_sig1 = 6 * rand_unif
f_sig2 = cos(x = 8 * pi * rand_unif)
f_sig3 = 0.5 * cos(x = 40 * pi * rand_unif)

f_sig = f_sig1 + f_sig2 + f_sig3

alpha = 2000
tau = 0
K = 3
DC = FALSE
init = 1
tol = 1e-6

set.seed(2)
res_1d = vmd(data = f_sig,
             alpha = alpha,
             tau = tau,
             K = K,
             DC = DC,
             init = init,
             tol = tol,
             verbose = FALSE)

#..............
# 2-dimensional
#..............

rows_cols = 10

set.seed(3)
data = matrix(runif(rows_cols^2), rows_cols, rows_cols)
alpha = 5000
tau = 0.25
K = 2
DC = TRUE
init = 1
tol = 1e-7

set.seed(4)
res_2d = vmd(data = data,
             alpha = alpha,
             tau = tau,
             K = K,
             DC = DC,
             init = init,
             tol = tol,
             verbose = FALSE)