Name

CDM_SPWVD_Analysis — Performs the analysis of a narrow band component of the RR signal using Smoothed Pseudo Wigner-Ville time-frequency distribution and complex demodulation methods.

Calling Sequence

result =
    CDM_SPWVD_Analysis(RR,Vt,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).
Vt
It can be:
  • either a real scalar: that gives the frequency reference (in Hz)
  • or a real vector of length N: The tidal volume respiratory signal. its slowly varying frequency gives the frequency reference.
See the ??? section for details.
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 ??? section for details.
filterlength
a real scalar with integer value: the order of the FIR Hamming band_pass filter. ??? section for details.
options
A struct with possible fields:
  • <term>lowpass</term>: 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]).
  • <term>timewindowlength</term>: 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.
  • <term>frequencywindowlength</term>: 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.
  • <term>frequencybins</term> a real scalar with positive integer power of 2 value. The number of frequency bins used in the smoothed pseudo Wigner-Ville distribution. The default value is 128.
results
a typed list with type "RRA" and with the following fields:
  • <term>time</term>: a real row vector of length N, the time discretization associated with the following signals.
  • <term>RR</term>: a real row vector of length N, the shifted RR signal
  • <term>Vt</term>: a real row vector of length N, the shifted Vt signal or the empty matrix.
  • <term>RRfiltered</term>: a real row vector of length N, the shifted band-pass filtered RR signal.
  • <term>IFreq_CDM</term>: a real row vector of length N, the instaneous frequency of the RR signal (in Hz) estimated by complex demodulation method.
  • <term>IFreq_SPWVD</term>: a real row vector of length N, the instaneous frequency of the RR signal (in Hz) estimated by smoothed pseudo Wigner-Ville method.
  • <term>IAmp_CDM</term>: a real row vector of length N, the instaneous amplitude of the RR signal estimated by complex demodulation method.
  • <term>IAmp_SPWVD</term>: a real row vector of length N, the instaneous amplitude of the RR signal estimated by smoothed pseudo Wigner-Ville method.
  • <term>IPhase</term>: a real row vector of length N, the evolution of the instaneous phase shift between the RR and Vt signals.
  • <term>IPow</term>: a real row vector of length N, the instaneous power of the RR signal.
  • <term>IDisp</term>: a real row vector of length N, the instaneous dispersion of the RR signal.

Description

CDM_SPWVD_Analysis Performs the analysis of a narrow band component of the RR signal using both Complex demodulation and Smoothed Pseudo Wigner-Ville time-frequency distribution methods. It estimates the instaneous frequency, the instanenous amplitude, the instaneous power and the instaneous dispersion of the RR signal. It allows a simple comparison of both methods.

Caution

  • <term>Vt</term> The results of CDM are reliable if the real Vt is available or if one can insure stable breathing in a narrow frequency band. In other cases SPWVD_Analysis is more suitable. See the examples below.

    The following demos illustrate cases of available Vt: Controlled breathing of control subject (RR & Vt), Controlled breathing of intensive care patient (RR & Vt), Free breathing of control subject (RR & Vt), Tilt test (RR & Vt), Apnea (RR & Vt). CDM and SPWVD instantaneous frequency and energy are superimposable.

    The following demos illustrate cases of not available Vt: Controlled breathing, control subject (RR), Handgrip (RR), Ergocycle (RR). For the controlled breathing, CDM and SPWVD instantaneous frequency and energy are superposable. But for ergocycle, the breathing frequency increase needs a large frequency band, [0.35 1.5] Hz, with a central frequency fixed to 0.7 Hz. In this case, CDM is not able to follow the real changes in breathing, unlike SPWVD.

  • <term>frequency_bounds</term> This argument is used to reduce the signal band around the effective respiratory frequency. the effective respiratory frequency can be estimated using the Time frequency interactive tool
  • <term>filterlength</term> The value of this parameter has to establish a compromise between the stiffness of the filter and the sensibility to local variations of the signal.

Examples

  • <term> Case where Vt is available </term>
        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=CDM_SPWVD_Analysis(RR,Vt,freq_sampling,frequency_bounds,filterlength); 
       
        PlotSpectralAnalysis(result,["RR","Vt"],"CDM decubitus_03")  // Draw results
        

    Produces:

  • <term> Case where Vt is not available and breathing frequency is varying</term>
        d=read(cardiovascularpath()+"demos/DATA/cycle_spont",-1,3)'; //read the data
        freq_sampling=8;
        RR=d(1,:);//RR signal
    
        filterlength=355;
        frequency_bounds=[0.35 1.5];
        f=scf(winsid()+1);
        res=CDM_SPWVD_Analysis(RR,0.7,freq_sampling,frequency_bounds,filterlength) 
        PlotSpectralAnalysis(res,"RR","Ergocycle rrpa-8Hz")  
        

    Produces:

    One can observe poor CDM results compared to SPWVD. SPWVD is here able to follow the frequency variations.

See Also

CDM_Analysis, CDM_SPWVD_Analysis, TimeMoments

Authors

Alessandro Monti, INRIA (scicos version)
Serge Steer, INRIA

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

TimeMoments ComplexDemodulation