Fetal monitoring and actocardiogram in the evaluation of fetal behavior

Correlation of fetal heart rate dynamics to inflammatory markers and brain-derived neurotrophic factor during pregnancy

OBJECTIVES

This study aims to show the relation between biomarkers in maternal and cord-blood samples and fetal heart rate variability (fHRV) metrics through a non-invasive fetal magnetocardiography (fMCG) technique.

METHODS

Twenty-three women were enrolled for collection of maternal serum and fMCG tracings immediately prior to their scheduled cesarean delivery. The umbilical cord blood was collected for measurement of biomarker levels. The fMCG metrics were then correlated to the biomarker levels from the maternal serum and cord blood.

RESULTS

Brain-derived neurotrophic factor (BDNF) had a moderate correlation with fetal parasympathetic activity (0.416) and fetal sympathovagal ratios (-0.309; -0.356). Interleukin (IL)-6 also had moderate-sized correlations but with an inverse relationship as compared to BDNF. These correlations were primarily in cord-blood samples and not in the maternal blood.

CONCLUSIONS

In this small sample-sized exploratory study, we observed a moderate correlation between fHRV and cord-blood BDNF and IL-6 immediately preceding scheduled cesarean delivery at term. These findings need to be validated in a larger population.

Evaluation of parameters for fetal behavioural state classification

Fetal behavioural states (fBS) describe periods of fetal wakefulness and sleep and are commonly defined by features such as body and eye movements and heart rate. Automatic state detection through algorithms relies on different parameters and thresholds derived from both the heart rate variability (HRV) and the actogram, which are highly dependent on the specific datasets and are prone to artefacts. Furthermore, the development of the fetal states is dynamic over the gestational period and the evaluation usually only separated into early and late gestation (before and after 32 weeks). In the current work, fBS detection was consistent between the classification algorithm and visual inspection in 87 fetal magnetocardiographic data segments between 27 and 39 weeks of gestational age. To identify how automated fBS detection could be improved, we first identified commonly used parameters for fBS classification in both the HRV and the actogram, and investigated their distribution across the different fBS. Then, we calculated a receiver operating characteristics (ROC) curve to determine the performance of each parameter in the fBS classification. Finally, we investigated the development of parameters over gestation through linear regression. As a result, the parameters derived from the HRV have a higher classification accuracy compared to those derived from the body movement as defined by the actogram. However, the overlapping distributions of several parameters across states limit a clear separation of states based on these parameters. The changes over gestation of the HRV parameters reflect the maturation of the fetal autonomic nervous system. Given the higher classification accuracy of the HRV in comparison to the actogram, we suggest to focus further research on the HRV. Furthermore, we propose to develop probabilistic fBS classification approaches to improve classification in less prototypical datasets.

Fetal assessment in buprenorphine-maintained women using fetal magnetoencephalography: a pilot study

BACKGROUND AND AIMS

In-utero exposure to opioids including buprenorphine (BUP) has been shown to affect fetal activity, specifically heart-rate variability (FHRV) and fetal movement (FM). Our objective was to extract simultaneous recordings of fetal cardiac and brain-related activity in BUP-maintained and non-opioid exposed pregnant women using a novel non-invasive biomagnetic technique.

DESIGN

A pilot study was conducted, recording and analyzing biomagnetic data from fetuses of BUP-maintained and non-opioid exposed pregnant women. Signals were acquired with the non-invasive 151-channel SARA (SQUID-Array for Reproductive Assessment) system. Advanced signal-processing techniques were applied to extract fetal heart and brain activity.

SETTING

University of Arkansas for Medical Sciences (UAMS, Little Rock, Arkansas, USA).

PARTICIPANTS

Eight BUP-maintained pregnant women from UAMS Women's Mental Health Program between gestational ages (GA) of 29-37 weeks who were treated with 8-24 mg of BUP daily. Sixteen pregnant women with no known opioid exposure in the same GA range were also included.

MEASUREMENTS

Outcome measures from the fetal heart and brain signals included: heart rate (FHR), FM, FHR accelerations, FHR-FM coupling, FHRV, fetal behavioral states (FBS) and power spectral density (PSD) of spontaneous brain activity. These measures were analyzed at three GA intervals.

FINDINGS

Fetal heart and brain activity parameters were extracted and quantified successfully from 18 non-opioid and 16 BUP recordings. Overall analysis in both groups show that: FHR and FM ranged from 131 to 141 beats per minute (b.p.m.) and 5 to 11 counts, respectively. In the 35-37 weeks GA, the coupling duration (~9 s) was the shortest, while three of the FHRV parameters were the highest. The PSD of brain activity revealed highest power in 0.5-4 Hz bandwidth. Transitions in FBS from quiet to active sleep were > 50% of sessions.

CONCLUSIONS

This pilot study showed that a novel biomagnetic technique allows simultaneous quantification of cardiac and brain activities of a group of buprenorphine-exposed and non-exposed fetuses in the third trimester.

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.