Nonlinear analysis of fetal heart rate dynamics in fetuses compromised by asymptomatic partial placental abruption

Placental abruption at near-term and term gestations: pathophysiology, epidemiology, diagnosis, and management

Placental abruption is the premature separation of the placenta from its uterine attachment before the delivery of a fetus. The clinical manifestations of abruption typically include vaginal bleeding and abdominal pain with a wide variety of abnormal fetal heart rate patterns. Clinical challenges arise when pregnant people with this condition present with profound vaginal bleeding, necessitating urgent delivery, especially when there is a concern for maternal and fetal compromise and coagulopathy. Abruption occurs in 0.6% to 1.2% of all pregnancies, with nearly half of abruption occurring at term gestations. An exposition of abruption at near-term (defined as the late preterm period from 34 0/7 to 36 6/7 weeks of gestation) and term (defined as ≥37 weeks of gestation) provides unique insights into its direct effects, as risks associated with preterm birth do not impact outcomes. Here, we explore the pathophysiology, epidemiology, and diagnosis of abruption. We discuss the interaction of chronic processes (decidual and uteroplacental vasculopathy) and acute processes (shearing forces applied to the abdomen) that underlie the pathophysiology. Risk factors for abruption and strengths of association are summarized. Sonographic findings of abruption and fetal heart rate tracings are presented. In addition, we propose a management algorithm for acute abruption that incorporates blood loss, vital signs, and urine output, among other factors. Lastly, we discuss blood component therapy, viscoelastic point-of-care testing, disseminated intravascular coagulopathy, and management of abruption complicated by fetal death. The review seeks to provide comprehensive, clinically focused guidance during a gestational age range when neonatal outcomes can often be favorable if rapid and evidence-based care is optimized.

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.

CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals

BACKGROUND

We developed CEPS as an open access MATLAB^® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements.

METHODS

Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system.

RESULTS

The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ ('tau') where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded from Bitbucket. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing.

CONCLUSIONS

We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing.

A Strategy for Classification of "Vaginal vs. Cesarean Section" Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

We propose objective and robust measures for the purpose of classification of "vaginal vs. cesarean section" delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.

Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics

This paper analyses the performance of SampEn and one of its derivatives, Fuzzy Entropy (FuzzyEn), in the context of artifacted blood glucose time series classification. This is a difficult and practically unexplored framework, where the availability of more sensitive and reliable measures could be of great clinical impact. Although the advent of new blood glucose monitoring technologies may reduce the incidence of the problems stated above, incorrect device or sensor manipulation, patient adherence, sensor detachment, time constraints, adoption barriers or affordability can still result in relatively short and artifacted records, as the ones analyzed in this paper or in other similar works. This study is aimed at characterizing the changes induced by such artifacts, enabling the arrangement of countermeasures in advance when possible. Despite the presence of these disturbances, results demonstrate that SampEn and FuzzyEn are sufficiently robust to achieve a significant classification performance, using records obtained from patients with duodenal-jejunal exclusion. The classification results, in terms of area under the ROC of up to 0.9, with several tests yielding AUC values also greater than 0.8, and in terms of a leave-one-out average classification accuracy of 80%, confirm the potential of these measures in this context despite the presence of artifacts, with SampEn having slightly better performance than FuzzyEn.

Complexity analysis of fetal heart rate preceding intrauterine demise

BACKGROUND

Visual non-stress test interpretation lacks the optimal specificity and observer-agreement of an ideal screening tool for intrauterine fetal demise (IUFD) syndrome prevention. Computational methods based on traditional heart rate variability have also been of limited value. Complexity analysis probes properties of the dynamics of physiologic signals that are otherwise not accessible and, therefore, might be useful in this context.

OBJECTIVE

To explore the association between fetal heart rate (FHR) complexity analysis and subsequent IUFD. Our specific hypothesis is that the complexity of the fetal heart rate dynamics is lower in the IUFD group compared with controls.

STUDY DESIGN

This case-control study utilized cases of IUFD at a single tertiary-care center among singleton pregnancies with at least 10min of continuous electronic FHR monitoring on at least 2 weekly occasions in the 3 weeks immediately prior to fetal demise. Controls delivered a live singleton beyond 35 weeks' gestation and were matched to cases by gestational age, testing indication, and maternal age in a 3:1 ratio. FHR data was analyzed using the multiscale entropy (MSE) method to derive their complexity index. In addition, pNNx, a measure of short-term heart rate variability, which in adults is ascribable primarily to cardiac vagal tone modulation, was also computed.

RESULTS

211 IUFDs occurred during the 9-year period of review, but only 6 met inclusion criteria. The median gestational age at the time of IUFD was 35.5 weeks. Three controls were matched to each case for a total of 24 subjects, and 87 FHR tracings were included for analysis. The median gestational age at the first fetal heart rate tracing was similar between groups (median [1st-3rd quartiles] weeks: IUFD cases: 34.7 (34.4-36.2); controls: 35.3 (34.4-36.1); p=.94). The median complexity of the cases' tracings was significantly less than the controls' (12.44 [8.9-16.77] vs. 17.82 [15.21-22.17]; p<.0001). Furthermore, the cases' median complexity decreased as gestation advanced whereas the controls' median complexity increased over time. However, this difference was not statistically significant [-0.83 (-2.03 to 0.47) vs. 0.14 (-1.25 to 0.94); p=.62]. The degree of short-term variability of FHR tracings, as measured by the pNN metric, was significantly lower (p<.005) for the controls (1.1 [0.8-1.3]) than the IUFD cases (1.3 [1.1-1.6]).

CONCLUSIONS

FHR complexity analysis using multiscale entropy analysis may add value to other measures in detecting and monitoring pregnancies at the highest risk for IUFD. The decrease in complexity and short-term variability seen in the IUFD cases may reflect perturbations in neuroautonomic control due to multiple maternal-fetal factors.