Skip to main content
Log in

Spectra of data sampled at frequency-modulated rates in application to cardiovascular signals: Part 1 analytical derivation of the spectra

  • Signal Processing
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Beat-to-beat cardiovascular signals, e.g. a series of systolic pressure values, can be considered as time series which are pulse amplitude modulated (PAM) and pulse frequency modulated (PFM). The latter process, due to variations in heart rate, causes the series to become non-uniformly spaced in time. If PAM is to be quantified by spectral analysis, the influence of PFM must be known. An analytical expression is therefore derived for the spectrum of sinusoids which are sampled according to the output event series of a linear integral pulse frequency modulator (IPFM). We conclude that two spectral components arise at the difference and sum of the PFM and PAM frequencies, fp±fx, with amplitudes proportional to the PFM modulation depth. These components appear as a DC component and as a first harmonic if both modulating frequencies are equal. In addition, a cluster of spectral components appears around the mean pulse frequency fo (i.e. mean heart rate), at frequencies fo-nfp±fx, which may leak into the signal band. From these theoretical considerations, we conclude that the amplitude spectrum of a sinusoidally varying systolic blood pressure series can contain up to 20–30% spurious components, owing to the heart rate modulation process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Abel, F. L., andWaldhausen, J. A. (1969): ‘Respiratory and cardiac effects on venous return,’Am. Heart J.,78, p. 266

    Article  Google Scholar 

  • Abramowitz, M., andStegum, I. A. (1970): ‘Handbook of mathematical functions’ (Dover, New York)

    Google Scholar 

  • Akselrod, S., Gordon, D., Madwed, J. B., Snidman, N. C., Shannon, D. C., andCohen, R. J. (1985): ‘Hemodynamic regulation: investigation by spectral analysis,’Am. J. Physiol.,249, pp. H867-H875

    Google Scholar 

  • Almasi, J. A., andSchmitt, O. H. (1974): ‘Basic technology of voluntary cardiorespiratory synchronization in electrophysiology,’IEEE Trans.,BME-21, pp. 264–273

    Google Scholar 

  • Baselli, G., Cerutti, S., Civardi, S., Liberati, D., Lombardi, F., Malliani, A., andPagani, M. (1986): ‘Spectral and cross-spectral analysis of heart rate and arterial blood pressure variability signals,’Comp. Biomed. Res.,19, pp. 520–534

    Article  Google Scholar 

  • Bayly, E. J. (1968): ‘Spectral analysis of pulse frequency modulation in the nervous systems,’IEEE Trans.,BME-15, pp. 257–265

    Google Scholar 

  • Berger, R. D., Akselrod, S., Gordon, D., andCohen, R. J. (1986): ‘An efficient algorithm for spectral analysis of heart rate variability,’ —Ibid.,BME-33, pp. 900–904

    Google Scholar 

  • Brigham, E. O. (1988): ‘The fast Fourier transform and its applications,’ (Prentice-Hall International)

  • Calvagno, G., andMunson, D. C., Jr, (1990): ‘New results on Yen’s approach to interpolation from nonuniformly spaced samples.’ Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp. 1535–1538

  • DeBoer, R. W., Karemaker, J. M., andStrackee, J. (1984): ‘Comparing spectra of a series of point events particularly for heart rate variability data,’IEEE Trans.,BME-31, pp. 384–387

    Google Scholar 

  • DeBoer, R. W., andKaremaker, J. M. (1985): ‘Relationships between short-term blood-pressure flucturations and heart-rate variability in resting subjects I: a spectral analysis approach,’Med. Biol. Eng. Comput.,23, pp. 352–358

    Article  Google Scholar 

  • Faes, Th. J. C., Lanting, P., TenVoorde, B. J., andRompelman, O. (1990): ‘The origin of respiratory sinus arrhythmia: towards a closed-loop identification of autonomic regulation in the cardiovascular system using respiratory induced fluctuations in heart rate and arterial blood pressure,’Automedica,13, pp. 33–44

    Google Scholar 

  • Gaster, M., andRoberts, J. B. (1977): ‘The spectral analysis of randomly sampled records by a direct transform,’Proc. R. Soc. Lond.,354, pp. 27–58

    Article  MathSciNet  Google Scholar 

  • Hildebrandt, G. (1953): ‘Über die rhytmische Funktionsordnung von Puls and Atem,’Z. Klin. Med.,150, pp. 445–454

    Google Scholar 

  • Hyndman, B. W., andMohn, R. K. (1975): ‘A model of the cardiac pacemaker and its use in decoding the information content of cardiac intervals,’Automedica,1, pp. 239–252

    Google Scholar 

  • Jenq, Y. C. (1988): ‘Digital spectra of non-uniformly sampled signals: Fundamentals and high-speed waveform digitizers,’IEEE Trans.,BME-37, pp. 245–251

    Google Scholar 

  • Kitney, R. I., andRompelman, O. (1987): ‘Beat-by-beat interrelationships between heart rate, blood pressure and respiration’in Kitney, R. I. andRompelman, O. (Eds.): ‘The beat-by-beat investigation of cardiovascular function’ (Clarendon Press, Oxford), pp. 146–177

    Google Scholar 

  • Nyquist, H. (1928): ‘Certain topics in telegraph transmission theory,’AIEE Trans., pp. 617–644

  • Oppenheim, A. V., andWillsky, A. S. (1983): ‘Signals and systems,’ (Prentice Hall, Englewood Cliffs, New Jersey)

    MATH  Google Scholar 

  • Pagani, M., Lombardi, F., Guzetti, S., Rimoldi, O., Furlan, R., Pizzinelli, P.,et al., (1986): ‘Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dogs,’Circ. Res.,59, pp. 178–193

    Google Scholar 

  • Papoulis, A. (1977): ‘Generalized sampling expansion,’IEEE Trans.,CAS-24, pp. 652–654

    MathSciNet  MATH  Google Scholar 

  • Rompelman, O., Coenen, A. J. R. M., andKitney, R. I. (1977): ‘Measurement of heart-rate variability: Part 1 Comparative study of heart-rate variability analysis methods,’Med. Biol. Eng. Comput.,15, pp. 233–239

    Article  Google Scholar 

  • Rompelman, O. (1986): ‘Tutorial review on prcessing the cardiac event series: a signal analysis approach,’Automedica,7, pp. 191–212

    Google Scholar 

  • Shannon, C. E. (1949): ‘Communication in the presence of noise,’Proc. IRE,37, pp. 10–21

    MathSciNet  Google Scholar 

  • Shapiro, H. S., andSilverman, R. A. (1960): ‘Alias-free sampling of random noise,’J. Soc. Ind. Appl. Math.,8, pp. 225–248

    Article  MathSciNet  MATH  Google Scholar 

  • TenVoorde, B. J., Ree, E. F., Hack, W. W. M., Bergschneider, V. M., Hoekstra, B. P. T., Faes, Th. J. C., andRompelman, O. (1990): ‘Spectral quantification of respiratory sinus arrhythmia in preterm and fullterm neonates: beyond half the mean heart rate,’Automedica,13, pp. 15–31

    Google Scholar 

  • TenVoorde, B. J., Faes, Th. J. C., andRompelman, O. (1994): ‘Spectra of data sampled at frequency modulated rates in application to cardiovascular signals: Part 2 evaluation of Fourier transform algorithms,’Med. Biol. Eng. Comput.,32 (1), pp. 71–76

    Article  Google Scholar 

  • Yen, J. L. (1956): ‘On nonuniform sampling of bandwidthlimited signals,’IRE Trans. Circuit Theory,CT-3, pp. 251–257

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

TenVoorde, B.J., Faes, T.J.C. & Rompelman, O. Spectra of data sampled at frequency-modulated rates in application to cardiovascular signals: Part 1 analytical derivation of the spectra. Med. Biol. Eng. Comput. 32, 63–70 (1994). https://doi.org/10.1007/BF02512480

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02512480

Keywords

Navigation