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Cardio Vascular toolbox >> Cardio Vascular toolbox > SPWVD_Analysis

SPWVD_Analysis

Performs the analysis of a narrow band component of the RR signal using Smoothed Pseudo Wigner-Ville time-frequency distribution method.

Calling Sequence

results =
SPWVD_Analysis(RR,aux,freq_sampling,frequency_bounds,filterlength
[,options])

Parameters

RR
a real vector of length N: The vector of interbeat intervals of the R waves in the electrocardiogram (ms).
aux
It can be:
  • a real vector of length N: an auxiliary signal that will be added to the result for visualization purposes. For example Vt, SBP, DBP, ...
  • an empty matrix [].
freq_sampling
a real positive scalar, the sampling frequency (in Hz) of the signals.
frequency_bounds
a real row vector of length 2: the frequency range of interest (in Hz). See the Caution section for details.
filterlength
a real scalar with integer value: the order of the FIR Hamming band_pass filter. Caution section for details.
options
A struct with possible fields:
  • lowpass: an odd length vector of real numbers. The coefficients of a fir lowpass filter used to smooth the resulting signals. The default value is computed by wfir("lp",195,[0.01 0],"hm",[0,0])
  • timewindowlength: a real scalar with positive integer odd value. The time smoothing window length used in the smoothed pseudo Wigner-Ville distribution. The default value is 77.
  • frequencywindowlength: a real scalar with positive integer odd value. The frequency smoothing window length used in the smoothed pseudo Wigner-Ville distribution. The default value is 75.
  • frequencybins a real scalar which value is a positive power of 2. The number of frequency bins used in the smoothed pseudo Wigner-Ville distribution. The default value is 128.
The results often are not very sensible to the option values around their default values.
results
a typed list with type "RRA" and with the following fields:
  • time: a real row vector of length N, the time discretization associated with the following signals.
  • RR: a real row vector of length N, the given RR signal
  • aux: a real row vector of length N, the given aux signal or the empty matrix.
  • RRfiltered: a real row vector of length N, the shifted band-pass filtered RR signal.
  • IFreq: a real row vector of length N, the instaneous frequency of the RR signal (in Hz).
  • IAmp: a real row vector of length N, the instaneous amplitude of the RR signal.
  • IPow: a real row vector of length N, the instaneous power of the RR signal.
  • IDisp: a real row vector of length N, the instaneous dispersion of the RR signal.

Description

SPWVD_Analysis Performs the analysis of a narrow band component of the RR signal using Smoothed Pseudo Wigner-Ville time-frequency distribution method. It estimates the instaneous frequency, the instanenous amplitude, the instaneous power and the instaneous dispersion of the RR signal.

The returned data are shifted to make their time evolution comparable and easily drawn using the PlotSpectralAnalysis function.

Caution

Examples

d=read(cardiovascularpath()+"demos/DATA/decubitus_03",-1,4)'; //read the data
freq_sampling=4;
RR=d(1,:);//RR signal
Vt=d(3,:);//Tidal Volume  signal

//narrow band filter
filterlength=355;
frequency_bounds=[0.25 1.9];

result=SPWVD_Analysis(RR,Vt,freq_sampling,frequency_bounds,filterlength);

PlotSpectralAnalysis(result,["RR","Vt"],"decubitus_03 SPWVD")  // Draw results

//using options
opt=struct();
opt.lowpass=wfir("lp",195,[0.1 0]/freq_sampling,"hm",[0,0]);
result=SPWVD_Analysis(RR,Vt,freq_sampling,frequency_bounds,filterlength);
PlotSpectralAnalysis(result,["RR","Vt"],"decubitus_03 SPWVD")  // Draw results

See Also

Authors

Bibliography

"Short-term control of the cardiovascular system: modelling and signal analysis", Alessandro Monti , Claire Médigue , Michel Sorine. Rapport de recherche n° 4427 April 12, 2002

"Instantaneous parameter estimation in cardiovascular time series by harmonic and time-frequency analysis", Alessandro Monti , Claire Médigue , Mangin Laurence,IEEE Trans Biomed Eng. 2002 Dec;49(12 Pt 2):1547-56.

"Instantaneous envelope and phase extraction from real signals: Theory, implementation, and an application to EEG analysis". Periklis Y. Ktonasa and Nicola Papp

Used Functions


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