Time-frequency analysis of fetal heartbeat fluctuation using wavelet transform

Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review

The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.

An estimate of fetal autonomic state by time-frequency analysis of fetal heart rate variability

In this study we present a noninvasive method that enables the investigation of the fetal heart rate (FHR) fluctuations. The objective was to design a quantitative measurement to assess the fetal autonomic nervous system and to investigate its development as a function of the gestational age. Our Medical Physics group has developed a complex algorithm for online beat-to-beat detection of the fetal ECG (FECG), extracted from the maternal abdominal ECG signal. We used our previously acquired FECG data, which includes noninvasive recordings of 200 maternal abdominal ECG signals. From these, we chose 35 cases of healthy pregnancies that we divided into three groups according to gestational age: Group 1, 23 +/- 2 wk; Group 2, 32 +/- 1 wk; and Group 3, 39 +/- 1 wk. The FHR variability was analyzed by a time-frequency decomposition based on a continuous wavelet transform. We showed that, independent of the gestational age, most of the FHR power is concentrated in the very-low-frequency range (0.02-0.08 Hz) and in the low-frequency range (0.08-0.2 Hz). In addition, there is power in the high-frequency range that correlates with the frequency range of fetal respiratory motion (0.4-1.7 Hz). In the intermediate-frequency range (0.2-0.4 Hz), the power is significantly smaller. The changes in the average power spectrum in relation to gestation time were carefully and quantitatively examined. The results imply that there is a neural organization during the last trimester of the pregnancy, and the sympathovagal balance is reduced with the gestational age.

Analysis of heartbeat dynamics by point process adaptive filtering

Heartbeats are a point process yet, most of the current analysis methods do not model this important characteristic of these data. We describe human heartbeat time series as a history dependent inverse Gaussian model. We present a point process adaptive filter algorithm to estimate the model's time-varying parameters, and use it to compute new measures of heart rate variability. We apply our algorithm to analyze simulated heartbeat data and actual heartbeat data from a tilt table experiment and from healthy subjects and subjects with congestive heart failure during sleep. Our results suggest a new approach for characterizing heartbeat dynamics.

A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability

Heart rate is a vital sign, whereas heart rate variability is an important quantitative measure of cardiovascular regulation by the autonomic nervous system. Although the design of algorithms to compute heart rate and assess heart rate variability is an active area of research, none of the approaches considers the natural point-process structure of human heartbeats, and none gives instantaneous estimates of heart rate variability. We model the stochastic structure of heartbeat intervals as a history-dependent inverse Gaussian process and derive from it an explicit probability density that gives new definitions of heart rate and heart rate variability: instantaneous R-R interval and heart rate standard deviations. We estimate the time-varying parameters of the inverse Gaussian model by local maximum likelihood and assess model goodness-of-fit by Kolmogorov-Smirnov tests based on the time-rescaling theorem. We illustrate our new definitions in an analysis of human heartbeat intervals from 10 healthy subjects undergoing a tilt-table experiment. Although several studies have identified deterministic, nonlinear dynamical features in human heartbeat intervals, our analysis shows that a highly accurate description of these series at rest and in extreme physiological conditions may be given by an elementary, physiologically based, stochastic model.

Fractal and periodic heart rate dynamics in fetal sheep: comparison of conventional and new measures based on fractal analysis

The physiological significance of spectral and fractal components of spontaneous heart rate (HR) variability in the fetus remains unclear. To examine the relationship between circadian rhythms in different measures of HR variability, R-R interval time series obtained by fetal ECGs were recorded continuously over 24 h in five pregnant sheep at 116-125 days gestation. Conventional measures of short-term (STV) and long-term variability (LTV), low-frequency (LF; 0.025-0.15 cycles/beat) and high-frequency (HF; 0.2-0.5 cycles/beat) spectral powers, the LF-to-HF ratio, and fractal dimension values were calculated from 24-h ECG recordings and quantified every 60 min. STV, LTV, and LF and HF spectral powers were minimal during the day but increased significantly to their highest values at night. We found a significant positive correlation between these measures, whereas the cosinor method showed significant similarity between their circadian rhythm patterns. Fetal R-R intervals also exhibited fractal structures. Fetal HR variability had a fractal structure, which was similar between day and night. These results suggested that the circadian rhythms exhibited by STV and LTV during the day were mainly due to changes in frequency components rather than to fractal components of fetal HR fluctuation.

Wavelet analysis of instantaneous heart rate: a study of autonomic control during thrombolysis

Myocardial infarction (MI) is known to elicit activation of the autonomic nervous system. Reperfusion, induced by thrombolysis, is thus expected to bring about a shift in the balance between the sympathetic and vagal systems, according to the infarct location. In this study, we explored the correlation between reperfusion and the spectral components of heart rate (HR) variability (HRV), which are associated with autonomic cardiac control. We analyzed the HR of patients during thrombolysis: nine anterior wall MI (AW-MI) and eight inferoposterior wall MI (IW-MI). Reperfusion was determined from changes in ST levels and reported pain. Reocclusion was detected in four patients. HRV was analyzed using a modified continuous wavelet transform, which provided time-dependent versions of the typically used low-frequency (LF) and high-frequency (HF) peaks and of their ratio, LF/HF. Marked alterations in at least one of the HRV parameters was found in all 18 reperfusion events. Patterns of HRV, compatible with a shift toward relative sympathetic enhancement, were found in all of the nine reperfusion events in IW-MI patients and in three AW-MI patients. Patterns of HRV compatible with relative vagal enhancement were found in six AW-MI patients (P < 0.001). Significant changes in HRV parameters were also found after reocclusion. Time-dependent spectral analysis of HRV using the wavelet transform was found to be valuable for explaining the patterns of cardiac rate control during reperfusion. In addition, examination of the entire record revealed epochs of markedly diminished HRV in two patients, which we attribute to vagal saturation.