Up: Spectral Analysis of Heart
Previous: Acknowledgements
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DeBoer RW, Karemaker JM, Strackee J.
Spectrum of a series of point events, generated by the integral pulse
frequency modulation model.
Med Biol Eng Comput 1985; 23:138-142.
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Berger RD, Akselrod S, Gordon D, Cohen RJ.
An efficient algorithm for spectral analysis of heart rate
variability.
IEEE Trans Biomed Eng 1986; BME-33:900-904.
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Moody GB.
ECG-based indices of physical activity.
In Computers in Cardiology 1992. Los Alamitos: IEEE Computer Society
Press, 1992; 403-406.
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Albrecht P, Cohen RJ.
Estimation of heart rate power spectrum bands from real-world data:
dealing with ectopic beats and noisy data.
In Computers in Cardiology 1988. Los Alamitos: IEEE Computer Society
Press, 1989; 311-314.
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Birkett CL, Kienzle MG, Myers GA.
Interpolation over ectopic beats increases low frequency power in
heart rate variability spectra.
In Computers in Cardiology 1991. Los Alamitos: IEEE Computer Society
Press, 1992; 257-259.
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Klieger RE, Miller JP, Bigger JT, Moss AJ, and the Multicenter Post-Infarct
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mortality after acute myocardial infarction.
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Lomb NR.
Least-squares frequency analysis of unequally spaced data.
Astrophysics and Space Science 1976; 39:447-462.
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Jones RH.
Fitting a continuous-time autoregression to unevenly sampled discrete
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Burr RL, Cowan MJ.
Autoregressive spectral models of heart rate variability.
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Fast algorithm for spectral analysis of unevenly sampled data.
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Press WH, Teukolsky SA, Vetterling WT, Flannery BP.
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Moody GB, Mark RG, Zoccola A, Mantero S.
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The C program below generates an instantaneous heart rate (HR) signal
suitable for Lomb PSD estimation. Its input should be a two-column
list of beat arrival times (in seconds) and beat type codes (1 for
normal beats, any other value for other types of beats). The output
contains a subset of the beat arrival times, with a sample of the HR
signal (in units of beats per minute) following each time. The scanf and printf statements may be replaced if different input
or output formats are required.
Note that this algorithm aggressively rejects intervals likely to be
outliers (whether due to ectopic beats, falsely detected beats,
missed beats, or simply mismeasured beat arrival times). When used to
derive a Lomb PSD estimate, this strategy works well, and permits
robust derivation of spectra even from highly corrupted time series.
When deriving FT or AR spectra, less stringent criteria must be used,
since the cost of deleting samples is high (either they must be
replaced, or the entire time series must be discarded).
#include <stdio.h>
#include <math.h>
#define NORMAL 1
#define OTHER 2
#define TOL 10 /* tolerance (bpm) */
main()
{
double ihr, ihrp, mhr = 70., t, tp;
int b, bp = OTHER;
while (scanf("%lf%d", &t, &b) == 2) {
if (b == NORMAL) {
ihr = 60./(t - tp);
mhr += (ihr - mhr)/10.;
if (bp == NORMAL &&
fabs(ihr - ihrp) < TOL &&
fabs(ihr - mhr) < TOL)
printf("%g %g\n", tp, ihr);
bp = NORMAL;
tp = t;
ihrp = ihr;
}
else
bp = OTHER;
}
}
Up: Spectral Analysis of Heart
Previous: Acknowledgements
George B. Moody
2002-04-22