Heart rate features in fetal behavioural states

Fetal states identification in cardiotocographic tracings through discrete emissions multivariate hidden Markov models

BACKGROUND AND OBJECTIVES

Computerized Cardiotocography (cCTG) allows to analyze the Fetal Heart Rate (FHR) objectively and thoroughly, providing valuable insights on fetal condition. A challenging but crucial task in this context is the automatic identification of fetal activity and quiet periods within the tracings. Different neural mechanisms are involved in the regulation of the fetal heart, depending on the behavioral states. Thereby, their correct identification has the potential to increase the interpretability and diagnostic capabilities of FHR quantitative analysis. Moreover, the most common pathologies in pregnancy have been associated with variations in the alternation between quiet and activity states.

METHODS

We address the problem of fetal states clustering by means of an unsupervised approach, resorting to the use of a multivariate Hidden Markov Models (HMM) with discrete emissions. A fixed length sliding window is shifted on the CTG traces and a small set of features is extracted at each slide. After an encoding procedure, these features become the emissions of a multivariate HMM in which quiet and activity are the hidden states. After an unsupervised training procedure, the model is used to automatically segment signals.

RESULTS

The achieved results indicate that our developed model exhibits a high degree of reliability in identifying quiet and activity states within FHR signals. A set of 35 CTG signals belonging to different pregnancies were independently annotated by an expert gynecologist and segmented using the proposed HMM. To avoid any bias, the physician was blinded to the results provided by the algorithm. The overall agreement between the HMM's predictions and the clinician's interpretations was 90%.

CONCLUSIONS

The proposed method reliably identified fetal behavioral states, the alternance of which is an important factor in the fetal development. One key strength of our approach lies in the ease of interpreting the obtained results. By utilizing a small set of parameters that are already used in cCTG and possess clear intrinsic meanings, our method provides a high level of explainability. Another significant advantage of our approach is its fully unsupervised learning process. The states identified by our model using the Baum-Welch algorithm are associated with the "Active" and "Quiet" states only after the clustering process, removing the reliance on expert annotations. By autonomously identifying the clusters based solely on the intrinsic characteristics of the signal, our method achieves a more objective evaluation that overcomes the limitations of subjective interpretations. Indeed, we believe it could be integrated in cCTG systems to obtain a more complete signal analysis.

Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals

In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate (FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing. Despite the availability of several approaches to analyze the variability of FHR signals (namely the FHRV), there are still shadows hindering a comprehensive understanding of how linear and nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose a straightforward processing and modeling route for a deeper understanding of the relationships between the characteristics of the FHR signal. A multiparametric modeling and investigation of the factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural networks. The obtained results show that linear features are more influential compared to nonlinear ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable and reliable information to clinicians and researchers.

The Relationship between Gestational Diabetes Metabolic Control and Fetal Autonomic Regulation, Movement and Birth Weight

(1) Background: Maternal metabolic control in gestational diabetes is suggested to influence fetal autonomic control and movement activity, which may have fetal outcome implications. We aimed to analyze the relationship between maternal metabolic control, fetal autonomic heart rate regulation, activity and birth weight. (2) Methods: Prospective noninterventional longitudinal cohort monitoring study accompanying 19 patients with specialist clinical care for gestational diabetes. Monthly fetal magnetocardiography with electro-physiologically-based beat-to-beat heart rate recording for analysis of heart rate variability (HRV) and the ‘fetal movement index’ (FMI) was performed. Data were compared to 167 healthy pregnant women retrieved from our pre-existing study database. (3) Results: Fetal vagal tone was increased with gestational diabetes compared to controls, whereas sympathetic tone and FMI did not differ. Within the diabetic population, sympathetic activation was associated with higher maternal blood-glucose levels. Maternal blood-glucose levels correlated positively with birth weight z scores. FMI showed no correlation with birth weight but attenuated the positive correlation between maternal blood-glucose levels and birth weight. (4) Conclusion: Fetal autonomic control is altered by gestational diabetes and maternal blood-glucose level, even if metabolic adjustment and outcome is comparable to healthy controls.

Maternal Serotonin Reuptake Inhibitor Antidepressants Have Acute Effects on Fetal Heart Rate Variability in Late Gestation

Background: Prenatal exposure to serotonin reuptake inhibitor (SRI) antidepressants increases risk for adverse neurodevelopmental outcomes, yet little is known about whether effects are present before birth. In relation to maternal SRI pharmacokinetics, this study investigated chronic and acute effects of prenatal SRI exposure on third-trimester fetal heart rate variability (HRV), while evaluating confounding effects of maternal depressed mood. Methods: At 36-weeks' gestation, cardiotocograph measures of fetal HR and HRV were obtained from 148 pregnant women [four groups: SRI-Depressed (n = 31), SRI-Non-Depressed (n = 18), Depressed (unmedicated; n = 42), and Control (n = 57)] before, and ~5-h after, typical SRI dose. Maternal plasma drug concentrations were quantified at baseline (pre-dose) and four time-points post-dose. Mixed effects modeling investigated group differences between baseline/pre-dose and post-dose fetal HR outcomes. Post hoc analyses investigated sex differences and dose-dependent SRI effects. Results: Maternal SRI plasma concentrations were lowest during the baseline/pre-dose fetal assessment (trough) and increased to a peak at the post-dose assessment; concentration-time curves varied widely between individuals. No group differences in fetal HR or HRV were observed at baseline/pre-dose; however, following maternal SRI dose, short-term HRV decreased in both SRI-exposed fetal groups. In the SRI-Depressed group, these post-dose decreases were displayed by male fetuses, but not females. Further, episodes of high HRV decreased post-dose relative to baseline, but only among SRI-Non-Depressed group fetuses. Higher maternal SRI doses also predicted a greater number of fetal HR decelerations. Fetuses exposed to unmedicated maternal depressed mood did not differ from Controls. Conclusions: Prenatal SRI exposure had acute post-dose effects on fetal HRV in late gestation, which differed depending on maternal mood response to SRI pharmacotherapy. Importantly, fetal SRI effects were sex-specific among mothers with persistent depressive symptoms, as only male fetuses displayed acute HRV decreases. At trough (pre-dose), chronic fetal SRI effects were not identified; however, concurrent changes in maternal SRI plasma levels suggest that fetal drug exposure is inconsistent. Acute SRI-related changes in fetal HRV may reflect a pharmacologic mechanism, a transient impairment in autonomic functioning, or an early adaption to altered serotonergic signaling, which may differ between males and females. Replication is needed to determine significance with postnatal development.

Dependencies between maternal and fetal autonomic tone

Background Disturbances in maternal physiology can cause changes in the fetal condition that may lead to impaired fetal development. Synchronous monitoring of cardiac autonomic tone via the assessment of the fetal and maternal heart rate (HR) and heart rate variability (HRV) may provide an appropriate diagnostic window. Methods Partial rank correlation coefficients between the maternal and fetal HR and HRV indices were calculated and verified by testing surrogate data in 315 magnetocardiographic (MCG) recordings from 141 healthy women pregnant with singleton fetuses [18+6 to 39+2 weeks gestational age (WGA)]. We assessed maternal self-perceived depression, anxiety and stress by means of the Depression, Anxiety, Stress Scales self-reporting instrument (DASS42G) questionnaire. Results The maternal HRV correlated positively with the fetal HRV, but negatively with the fetal HR. Correlation was |r|<0.2 in state-independent and gestational age (GA) <32 weeks, but |r|>0.2 in active sleep and GA ≥32 weeks. The DASS42G results correlated with the maternal HRV and HR, while the fetal HR and HRV were not influenced. Conclusion Correlations between maternal and fetal autonomic activation were statistically confirmed. They depend on the GA and active fetal state. As far as healthy subjects are concerned, maternal self-perceived stress, anxiety or depression is mirrored in maternal but not in fetal autonomic tone.

Developmental milestones of the autonomic nervous system revealed via longitudinal monitoring of fetal heart rate variability

BACKGROUND

Fetal heart rate variability (fHRV) of normal-to-normal (NN) beat intervals provides high-temporal resolution access to assess the functioning of the autonomic nervous system (ANS).

AIM

To determine critical periods of fetal autonomic maturation. The developmental pace is hypothesized to change with gestational age (GA).

STUDY DESIGN

Prospective longitudinal observational study.

SUBJECTS

60 healthy singleton fetuses were followed up by fetal magnetocardiographic heart rate monitoring 4-11 times (median 6) during the second half of gestation.

OUTCOME MEASURE

FHRV parameters, accounting for differential aspects of the ANS, were studied applying linear mixed models over four predefined pregnancy segments of interest (SoI: <27; 27+0-31+0; 31+1-35+0; >35+1 weeks GA). Periods of fetal active sleep and quiescence were accounted for separately.

RESULTS

Skewness of the NN interval distribution VLF/LF band power ratio and complexity describe a saturation function throughout the period of interest. A decreasing LF/HF ratio and an increase in pNN5 indicate a concurrent shift in sympathovagal balance. Fluctuation amplitude and parameters of short-term variability (RMSSD, HF band) mark a second acceleration towards term. In contrast, fetal quiescence is characterized by sequential, but low-margin transformations; ascending overall variability followed by an increase of complexity and superseded by fluctuation amplitude.

CONCLUSIONS

An increase in sympathetic activation, connected with by a higher ability of parasympathetic modulation and baseline stabilization, is reached during the transition from the late 2nd into the early 3rd trimester. Pattern characteristics indicating fetal well-being saturate at 35 weeks GA. Pronounced fetal breathing efforts near-term mirror in fHRV as respiratory sinus arrhythmia.

Monitoring fetal maturation-objectives, techniques and indices of autonomic function

Monitoring the fetal behavior does not only have implications for acute care but also for identifying developmental disturbances that burden the entire later life. The concept, of 'fetal programming', also known as 'developmental origins of adult disease hypothesis', e.g. applies for cardiovascular, metabolic, hyperkinetic, cognitive disorders. Since the autonomic nervous system is involved in all of those systems, cardiac autonomic control may provide relevant functional diagnostic and prognostic information. The fetal heart rate patterns (HRP) are one of the few functional signals in the prenatal period that relate to autonomic control and, therefore, is predestinated for its evaluation. The development of sensitive markers of fetal maturation and its disturbances requires the consideration of physiological fundamentals, recording technology and HRP parameters of autonomic control. Based on the ESGCO2016 special session on monitoring the fetal maturation we herein report the most recent results on: (i) functional fetal autonomic brain age score (fABAS), Recurrence Quantitative Analysis and Binary Symbolic Dynamics of complex HRP resolve specific maturation periods, (ii) magnetocardiography (MCG) based fABAS was validated for cardiotocography (CTG), (iii) 30 min recordings are sufficient for obtaining episodes of high variability, important for intrauterine growth restriction (IUGR) detection in handheld Doppler, (iv) novel parameters from PRSA to identify Intra IUGR fetuses, (v) evaluation of fetal electrocardiographic (ECG) recordings, (vi) correlation between maternal and fetal HRV is disturbed in pre-eclampsia. The reported novel developments significantly extend the possibilities for the established CTG methodology. Novel HRP indices improve the accuracy of assessment due to their more appropriate consideration of complex autonomic processes across the recording technologies (CTG, handheld Doppler, MCG, ECG). The ultimate objective is their dissemination into routine practice and studies of fetal developmental disturbances with implications for programming of adult diseases.

Quantifying the Interactions between Maternal and Fetal Heart Rates by Transfer Entropy

Evidence of the short term relationship between maternal and fetal heart rates has been found in previous studies. However there is still limited knowledge about underlying mechanisms and patterns of the coupling throughout gestation. In this study, Transfer Entropy (TE) was used to quantify directed interactions between maternal and fetal heart rates at various time delays and gestational ages. Experimental results using maternal and fetal electrocardiograms showed significant coupling for 63 out of 65 fetuses, by statistically validating against surrogate pairs. Analysis of TE showed a decrease in transfer of information from fetus to the mother with gestational age, alongside the maturation of the fetus. On the other hand, maternal to fetal TE was significantly greater in mid (26-31 weeks) and late (32-41 weeks) gestation compared to early (16-25 weeks) gestation (Mann Whitney Wilcoxon (MWW) p<0.05). TE further increased from mid to late, for the fetuses with RMSSD of fetal heart rate being larger than 4 msec in the late gestation. This difference was not observed for the fetuses with smaller RMSSD, which could be associated with the quiet sleep state. Delay in the information transfer from mother to fetus significantly decreased (p = 0.03) from mid to late gestation, implying a decrease in fetal response time. These changes occur concomitant with the maturation of the fetal sensory and autonomic nervous systems with advancing gestational age. The effect of maternal respiratory rate derived from maternal ECG was also investigated and no significant relationship was found between breathing rate and TE at any lag. In conclusion, the application of TE with delays revealed detailed information on the fetal-maternal heart rate coupling strength and latency throughout gestation, which could provide novel clinical markers of fetal development and well-being.

A computer-aided approach to detect the fetal behavioral states using multi-sensor Magnetocardiographic recordings

We propose a novel computational approach to automatically identify the fetal heart rate patterns (fHRPs), which are reflective of sleep/awake states. By combining these patterns with presence or absence of movements, a fetal behavioral state (fBS) was determined. The expert scores were used as the gold standard and objective thresholds for the detection procedure were obtained using Receiver Operating Characteristics (ROC) analysis. To assess the performance, intraclass correlation was computed between the proposed approach and the mutually agreed expert scores. The detected fHRPs were then associated to their corresponding fBS based on the fetal movement obtained from fetal magnetocardiogaphic (fMCG) signals. This approach may aid clinicians in objectively assessing the fBS and monitoring fetal wellbeing.

Heart rate variability parameters and fetal movement complement fetal behavioral states detection via magnetography to monitor neurovegetative development

Fetal behavioral states are defined by fetal movement and heart rate variability (HRV). At 32 weeks of gestational age (GA) the distinction of four fetal behavioral states represented by combinations of quiet or active sleep or awakeness is possible. Prior to 32 weeks, only periods of fetal activity and quiesence can be distinguished. The increasing synchronization of fetal movement and HRV reflects the development of the autonomic nervous system (ANS) control. Fetal magnetocardiography (fMCG) detects fetal heart activity at high temporal resolution, enabling the calculation of HRV parameters. This study combined the criteria of fetal movement with the HRV analysis to complete the criteria for fetal state detection. HRV parameters were calculated including the standard deviation of the normal-to-normal R–R interval (SDNN), the mean square of successive differences of the R–R intervals (RMSSD, SDNN/RMSSD ratio, and permutation entropy (PE) to gain information about the developing influence of the ANS within each fetal state. In this study, 55 magnetocardiograms from healthy fetuses of 24–41 weeks’ GA were recorded for up to 45 min using a fetal biomagnetometer. Fetal states were classified based on HRV and movement detection. HRV parameters were calculated for each state. Before GA 32 weeks, 58.4% quiescence and 41.6% activity cycles were observed. Later, 24% quiet sleep state (1F), 65.4% active sleep state (2F), and 10.6% active awake state (4F) were observed. SDNN increased over gestation. Changes of HRV parameters between the fetal behavioral states, especially between 1F and 4F, were statistically significant. Increasing fetal activity was confirmed by a decrease in PE complexity measures. The fHRV parameters support the differentiation between states and indicate the development of autonomous nervous control of heart rate function.

New estimators and guidelines for better use of fetal heart rate estimators with Doppler ultrasound devices

Characterizing fetal wellbeing with a Doppler ultrasound device requires computation of a score based on fetal parameters. In order to analyze the parameters derived from the fetal heart rate correctly, an accuracy of 0.25 beats per minute is needed. Simultaneously with the lowest false negative rate and the highest sensitivity, we investigated whether various Doppler techniques ensure this accuracy. We found that the accuracy was ensured if directional Doppler signals and autocorrelation estimation were used. Our best estimator provided sensitivity of 95.5%, corresponding to an improvement of 14% compared to the standard estimator.

Linear and nonlinear measures of fetal heart rate patterns evaluated on very short fetal magnetocardiograms

We analyzed the effectiveness of linear short- and long-term variability time domain parameters, an index of sympatho-vagal balance (SDNN/RMSSD) and entropy in differentiating fetal heart rate patterns (fHRPs) on the fetal heart rate (fHR) series of 5, 3 and 2 min duration reconstructed from 46 fetal magnetocardiograms. Gestational age (GA) varied from 21 to 38 weeks. FHRPs were classified based on the fHR standard deviation. In sleep states, we observed that vagal influence increased with GA, and entropy significantly increased (decreased) with GA (SDNN/RMSSD), demonstrating that a prevalence of vagal activity with autonomous nervous system maturation may be associated with increased sleep state complexity. In active wakefulness, we observed a significant negative (positive) correlation of short-term (long-term) variability parameters with SDNN/RMSSD. ANOVA statistics demonstrated that long-term irregularity and standard deviation of normal-to-normal beat intervals (SDNN) best differentiated among fHRPs. Our results confirm that short- and long-term variability parameters are useful to differentiate between quiet and active states, and that entropy improves the characterization of sleep states. All measures differentiated fHRPs more effectively on very short HR series, as a result of the fMCG high temporal resolution and of the intrinsic timescales of the events that originate the different fHRPs.

Phase plane based identification of fetal heart rate patterns

Using a phase plane analysis (PPA) of the spatial spread of trajectories of the fetal heart rate and its time-derivative we characterize the fetal heart rate patterns (fHRP) as defined by Nijhuis. For this purpose, we collect 22 fetal magnetocardiogram using a 151 SQUID system from 22 low-risk fetuses in gestational ages ranging from 30 to 37 weeks. Each study lasted for 30 minutes. After the attenuation of the maternal cardiac signals, we identify the R waves using an adaptive Hilbert transform approach and calculate the fetal heart rate. On these datasets, we apply the proposed approach and the traditionally used approaches such as standard deviation of the normal to normal intervals (SDNN) and root mean square of the successive difference (RMSSD). Heart rate patterns are scored by an expert using Nijhuis criteria and revealed A, B, and D patterns. A receiver operator characteristic (ROC) curve is used to assess the performance of the metric to differentiate the different patterns. Results showed that only PPA was able to differentiate all pairs of fHRP with high performance.

A novel approach to track fetal movement using multi-sensor magnetocardiographic recordings

Changes in fetal magnetocardiographic (fMCG) signals are indicators for fetal body movement. We propose a novel approach to reliably extract fetal body movements based on the field strength of the fMCG signal independent of its frequency. After attenuating the maternal MCG, we use a Hilbert transform approach to identify the R-wave. At each R-wave, we compute the center-of-gravity (cog) of the coordinate positions of MCG sensors, each weighted by the magnitude of the R-wave amplitude recorded at the corresponding sensor. We then define actogram as the distance between the cog computed at each R-wave and the average of the cog from all the R-waves in a 3-min duration. By applying a linear de-trending approach to the actogram we identify the fetal body movement and compare this with the synchronous occurrence of the acceleration in the fetal heart rate. Finally, we apply this approach to the fMCG recorded simultaneously with ultrasound from a single subject and show its improved performance over the QRS-amplitude based approach in the visually verified movements. This technique could be applied to transform the detection of fetal body movement into an objective measure of fetal health and enhance the predictive value of prevalent clinical testing for fetal wellbeing.