Computerized analysis of fetal heart rate variability signal during the stages of labor : cCTG analysis during labor

Approximate entropy (ApEn) is a recently developed statistic quantifying regularity and complexity, which appears to have potential application to a wide variety of relatively short (greater than 100 points) and noisy time-series data. The development of ApEn was motivated by data length constraints commonly encountered, e.g., in heart rate, EEG, and endocrine hormone secretion data sets. We describe ApEn implementation and interpretation, indicating its utility to distinguish correlated stochastic processes, and composite deterministic/ stochastic models. We discuss the key technical idea that motivates ApEn, that one need not fully reconstruct an attractor to discriminate in a statistically valid manner-marginal probability distributions often suffice for this purpose. Finally, we discuss why algorithms to compute, e.g., correlation dimension and the Kolmogorov-Sinai (KS) entropy, often work well for true dynamical systems, yet sometimes operationally confound for general models, with the aid of visual representations of reconstructed dynamics for two contrasting processes. (c) 1995 American Institute of Physics.

The powers of the low-frequency (LF) and high-frequency (HF) oscillations characterizing heart rate variability (HRV) appear to reflect, in their reciprocal relationship, changes in the state of the sympathovagal balance occurring during numerous physiological and pathophysiological conditions. However, no adequate information is available on the quantitative resolution of this methodology. We studied 22 healthy volunteers (median age, 46.5 years) who were subjected after a rest period to a series of passive head-up tilt steps randomly chosen from the following angles: 15 degrees, 30 degrees, 45 degrees, 60 degrees, and 90 degrees. From the continuous ECG, after appropriate analog-to-digital conversion, a personal computer was used to compute, with an autoregressive methodology, time and frequency domain indexes of RR interval variability. Spectral and cross-spectral analysis with the simultaneously recorded respiratory signal excluded its contribution to LF. Age was significantly correlated to variance and to the absolute values in milliseconds squared of very-low-frequency (VLF), LF, and HF components. The tilt angle was correlated to both LF and HF (expressed in normalized units [nu]) and to the LF-to-HF ratio (r = .78, -.72, and .68; respectively). Lower levels of correlation were found with HF (in ms2) and RR interval. No correlation was present between tilt angle and variance, VLF, or LF (in ms2). Individual analysis confirmed that the use of nu provided the greatest consistency of results. Spectral analysis of HRV, using nu or LF-to-HF ratio, appears to be capable of providing a noninvasive quantitative evaluation of graded changes in the state of the sympathovagal balance.