Abstract
The clinical diagnosis of neonatal sepsis is preceded by abnormal heart rate (HR) characteristics of transient decelerations and reduced variability, which intuitively appear to be more nonstationary than normal HR variability. Our goals were to investigate stationarity of HR, and to devise measures useful for early diagnosis of neonatal sepsis. In this context, we define non-stationarity to be present when the observed data differ from surrogate data generated by stationary Gaussian noise with arbitrary linear correlations. We devised statistical methods for determining stationarity of HR data based on the two-sample Kolmogorov–Smirnov (KS) test. We compared distributions of KS distances between small sample epochs from clinical data with those of isospectral surrogates and of surrogates generated using the amplitude-adjusted Fourier transform technique, reasoning that they should differ significantly for nonstationary data. We found significant evidence of non-stationarity for records longer than 1 min. We developed new HR measures based on the empirical cumulative distribution function (ECDF) that are highly significantly associated with sepsis, but are not correlated with HR measures such as moments or sample entropy. We conclude that neonatal HR data cannot be assumed to be stationary, and become even less stationary prior to sepsis.
Similar content being viewed by others
References
Aghili, A. A., Rizwan-uddin, M. P. Griffin, and J. R. Moorman. Scaling and ordering of neonatal heart rate variability. Phys. Rev. Lett. 74:1254-1257, 1995.
Bamber, D. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. J. Math. Psychol. 12:387-415, 1975.
Bendat, J. S., and A. G. Piersol. Random data. Anal. Meas. Procedures 2:566, 1986.
Chang, K. L., K. J. Monahan, M. P. Griffin, D. E. Lake, and J. R. Moorman. Comparison and clinical application of frequency domain methods in analysis of neonatal heart rate time series. Ann. Biomed. Eng. 29:764-774, 2001.
Epps, T. W. Testing that a Gaussian process is stationary. Ann. Stat. 16: 1667-1683, 1988.
Garde, S., M. G. Regalado, V. L. Schechtman, and M. C. Khoo. Nonlinear dynamics of heart rate variability in cocaine-exposed neonates during sleep. Am. J. Physiol. Heart Circ. Physiol. 280:H2920-H2928, 2001.
Griffin, M. P., and J. R. Moorman. Toward the early diagnosis of neonatal sepsis and sepsis-like illness using novel heart rate analysis. Pediatrics 107:97-104, 2001.
Griffin, M. P., T. M. O'Shea, E. A. Bissonette, F. E. Harrell Jr., D. E. Lake, and J. R. Moorman. Abnormal heart rate characteristics preceding neonatal sepsis and sepsis-like illness. Pediatr. Res. 53:920-926, 2003.
Grossman, P. Breathing rhythms of the heart in a world of no steady state: A comment on Weber, Molenaar and van der Molen. Psychophysiology 29: 66-72, 1992.
Grossman, P., B. J. van, and C. Wientjes. A comparison of three quantification methods for estimation of respiratory sinus arrhythmia. Psychophysiology 27:702-714, 1990.
Hanley, J. A., and B. J. McNeil. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29-36, 1982.
Hartmut, Mi. Testing stationarity in the mean of autoregressive processes with a nonparametric regression trend. Ann. Stat. 20:1426-1440, 1992.
Hoyer, D., and L. S. Liebovitch. Practical problems of determining the dimensions of heart rate data. Med. Biol. Eng. Comput. 35:27-32, 1997.
Kim, D. A. Bayesian significance test of the stationarity of regression parameters. Biometrika 78:667-675, 1991.
Kolmogorov, A. Sulla determinazione empirica di una legge di distribuzione. Ist. Ital. Attuari. G. 4:1-11, 1933.
Korhonen, I. Comparison of linear and nonlinear analysis of heart rate variability in sedated cardiac surgery patients. In: 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, October 25–28, 2001.
Kovatchev, B. P., L. S. Farhy, H. Cao, M. P. Griffin, D. E. Lake, and J. R. Moorman. Sample asymmettry analysis of heart rate characteristics with application to neonatal sepsis and systemic inflammatory response syndrome. Ped. Res. 54:892-898, 2003.
Kuusela, T. A. Nonlinear methods of biosignal analysis in assessing terbutaline-induced heart rate and blood pressure changes. Am. J. Physiol. Heart Circ. Physiol. 282:H773-H781, 2002.
Lake, D. E. Efficient adaptive signal estimation and signal dimension estimation using piecewise projection libraries. In: Wavelet Applications, edited by V. H. Szu, Proceedings of the SPIE 3391:388-395, 1998.
Lake, D. E., J. S. Richman, M. P. Griffin, and J. R. Moorman. Sample entropy analysis of neonatal heart rate variability. Am. J. Physiol. 283:R789-R797, 2002.
Mitov, I. P., and I. K. Daskalov. Power spectra accuracy improvement by optimal signal epoch selection: An heuristic approach. Med. Eng. Phys. 19:380-385, 1997.
Moody, G. B. ECG-based indices of physical activity. Comput. Cardiol. 19:406, 1992.
Nelson, J. C., Rizwan-uddin, M. P. Griffin, and J. R. Moorman. Probing the order within neonatal heart rate variability. Pediatr. Res. 43:823-831, 1998.
Pilgram, B., and D. T. Kaplan. Nonstationarity and 1/f noise characteristics in heart rate. Am. J. Physiol. 276:R1-R9, 1999.
Pola, S. Estimation of the power spectral density in nonstationary cardiovascular time series: Assessing the role of the time-frequency representations (TFR). IEEE Trans. Biomed. Eng. 43:46-59, 1996.
Priestley, M. B., and T. Subba Rao. A test for nonstationarity of time-series. J. R. Stat. Soc. 31:140-149, 1969.
Richman, J. S., and J. R. Moorman. Physiological time series analysis using approximate entropy and sample entropy. Am. J. Physiol. 278:H2039-H2049, 2000.
Schreiber, T. Constrained randomization of time series data. Phys. Rev. Lett. 80:2105-2108, 1998.
Schreiber, T., and A. Schmitz. Surrogate time series. Phys. D 142:346-382, 2000.
Smirnov, N. V. On the estimation of the discrepancy between empirical curves of distributions for two independent samples. Bull. Math. Univ. Moscou. 2, 1939.
Smirnov, N. V. Obuklonenijah empiricheskoi krivoi raspredelenija. Recueil Math. Mat. Sbornik, N. S. 6:13-26, 1939.
Stoll, B. J., T. Gordon, S. B. Korones, S. Shankaran, J. E. Tyson, C. R. Bauer, A. A. Fanaroff, J. A. Lemons, E. F. Donovan, W. Oh, D. K. Stevenson, R. A. Ehrenkranz, L. A. Papile, J. Verter, and L. L. Wright. Late-onset sepsis in very low birth weight neonates: A report from the National Institute of Child Health and Human Development Neonatal Research Network. J. Pediatr. 129:63-71, 1996.
Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation 93:1043-1065, 1996.
Theiler, J., S. Eubank, A. Longtin, B. Galdrikian, and J. D. Farmer. Testing for nonlinearity in time series: The method of surrogate data. Phys. D 58:77-94, 1992.
Viniotis, Y. Random Processes. Probability and Random Processes for Electrical Engineering. McGraw-Hill, 401, 1998.
Weber, E. J. A nonstationarity test for the spectral analysis of physiological time series with an application to respiratory sinus arrhythmia. Psychophysiology 29:55-65, 1992.
Zwiener, U., D. Hoyer, B. Luthke, K. Schmidt, and R. Bauer. Relations between parameters of spectral power densities and deterministic chaos of heart-rate variability. J. Auton. Nerv. Syst. 57:132-135, 1996.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Cao, H., Lake, D.E., Griffin, M.P. et al. Increased Nonstationarity of Neonatal Heart Rate Before the Clinical Diagnosis of Sepsis. Annals of Biomedical Engineering 32, 233–244 (2004). https://doi.org/10.1023/B:ABME.0000012743.81754.0b
Issue Date:
DOI: https://doi.org/10.1023/B:ABME.0000012743.81754.0b