Coupling Analysis of Fetal and Maternal Heart Rates via Transfer Entropy Using Magnetocardiography.

Recent studies have shown that occasional short term coupling between fetal and maternal cardiac systems occurs. Fetal magnetocardiography (fMCG) is a non-invasive technique that records the magnetic fields associated with the electrical activity of the fetal heart through sensors placed over the maternal abdomen. The fMCG allows accurate estimation of fetal heart rates (fHR) due to its high signal-to-noise ratio (SNR) and temporal resolution. In this study, we analyzed couplingbetween fHR and maternal heart rates (mHR) using Transfer Entropy (TE). TE determines coupling between two variables by quantifying the information transferred between them in both directions. In this work, we used 74 fMCG recordings to compute TE in both directions over 1-minute disjoint time windows (TW). We examined the effect of fetal movement (FM) as a factor of influence on the TE analysis. We identified 21 subjects with FM during the recording and separated them into two gestational age (GA) groups (GA1<32 and GA2≥32 weeks). Next, TE values were compared between TWs containing non- FM with TWs containing FM using Wilcoxon Signed-Rank test. In addition, we compared TE calculations for non-FM segments obtained from the 74 subjects using Rank-Sum test in the two GA groups. Our results showed that TE values from TWs containing FM are not significantly different than those computed for TWs of non-FM. In both directions, we found that TE values obtained from the 74 subjects did not show any significant difference between GA1 and GA2 which is consistent with previous studies. Our study suggests that FM does not affect the TE computations.

Selection of Reference Channels Based on Mutual Information for Frequency-Dependent Subtraction Method Applied to Fetal Biomagnetic Signals.

OBJECTIVE: We propose a method that uses minimal redundancy and maximal relevance (mRMR) based on mutual information as criteria to automatically select references for the frequency-dependent subtraction (SUBTR) method to attenuate maternal (mMCG) and fetal (fMCG) magnetocardiograms of fetal magnetoencephalography recordings. METHODS: mRMR is calculated between all channels and mMCG/fMCG target channels and the most promising sensors are used as references to perform SUBTR. We measured the performance of SUBTR at removing interferences in two steps for different number of references in 38 real datasets. The evaluation was based on the MCG amplitude reduction. We compared the performance of the mRMR approach with random selection of references. RESULTS: Significant differences in interference removal were found when a distinct number of references were chosen by mRMR compared to random selection. CONCLUSION: mRMR provides an effective tool to automatically select a set of featured references. SIGNIFICANCE: Although we show the utility of the mRMR method to biomagnetic signals, the approach can easily be adapted to sensor array data from other applications.

Cardiac time intervals derived by magnetocardiography in fetuses exposed to pregnancy hypertension syndromes.

OBJECTIVE: To test the hypothesis that fetuses exposed to maternal preeclampsia or chronic hypertension have deranged development of cardiac time intervals. STUDY DESIGN: Pregnancies were divided into three groups: Intrauterine Growth Restricted (IUGR), Hypertensive, and Normal. Each group's mean fetal cardiac time intervals (P, PR, QRS and RR) derived by magnetocardiography were calculated using an analysis of covariance model's regression-adjusted estimates for a gestational age of 35 weeks. RESULTS: We reviewed 141 recordings from 21 IUGR, 46 Hypertensive and 74 Normal patients. The IUGR, Hypertensive and Normal groups, respectively, had adjusted mean intervals in milliseconds of 66.4, 66.8 and 76.2 for P (P=0.001), 95.9, 101.6 and 109.6 for PR (P=0.002), 77.2, 78.7 and 78.7 for QRS (P=0.81) and 429.8, 429.2 and 428.5 for RR (P=0.97). CONCLUSION: P and PR intervals are abbreviated in normotrophic fetuses exposed to maternal hypertension, suggesting shortened atrioventricular conduction times.

Genetic algorithms for dipole location of fetal magnetocardiography.

In this paper, we explore the use of Maximum Likelihood (ML) method with Genetic Algorithms (GA) as global optimization procedure for source reconstruction in fetal magnetocardiography (fMCG) data. A multiple equivalent current dipole (ECD) model was used for sources active in different time samples. Inverse solutions across time were obtained for a single-dipole approximation to estimate the trajectory of the dipole position. We compared the GA and SIMPLEX methods in a simulation environment under noise conditions. Methods are applied on a real fMCG data. Results show robust estimators of the cardiac sources when GA is used as optimization technique.

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.

Quantifying risks of preterm birth in the Arkansas Medicaid population, 2001-2005.

OBJECTIVE: The objective of this study was to examine risks of preterm births, quantify the explanatory power achieved by adding medical and obstetric risk factors to the models and to examine temporal changes in preterm birth due to changes in Medicaid eligibility and the establishment of a maternal-fetal medicine referral system. STUDY DESIGN: The study used data from the 2001 to 2005-linked Arkansas (AR) Medicaid claims and birth certificates of preterm and term singleton deliveries (N=89 459). Logistic regression modeled the association among gestational age, demographic characteristics and risk factors, pooled and separately by year. RESULT: Physiological risk factors were additive with demographic factors and explained more of the preterm birth ≤32 weeks than later preterm birth. Changing eligibility requirements for Medicaid recipients and increasing the financial threshold from 133 to 200% of federal poverty level had an impact on temporal changes. The proportion of births ≤32 weeks declined to 33%, from 3.0 to 2.0. However, later preterm births declined and then increased in the last year. CONCLUSION: Physiological conditions are strongly associated with early preterm birth. Maternal behaviors and other stressors are predictive of later preterm birth. Unmeasured effects of poverty continue to have a role in preterm birth. Further examination of the referral system is needed.

Removal of interference from fetal MEG by frequency dependent subtraction.

Fetal magnetoencephalography (fMEG) recordings are contaminated by maternal and fetal magnetocardiography (MCG) signals and by other biological and environmental interference. Currently, all methods for the attenuation of these signals are based on a time-domain approach. We have developed and tested a frequency dependent procedure for removal of MCG and other interference from the fMEG recordings. The method uses a set of reference channels and performs subtraction of interference in the frequency domain (SUBTR). The interference-free frequency domain signals are converted back to the time domain. We compare the performance of the frequency dependent approach with our present approach for MCG attenuation based on orthogonal projection (OP). SUBTR has an advantage over OP and similar template approaches because it removes not only the MCG but also other small amplitude biological interference, avoids the difficulties with inaccurate determination of the OP operator, provides more consistent and stable fMEG results, does not cause signal redistribution, and if references are selected judiciously, it does not reduce fMEG signal amplitude. SUBTR was found to perform well in simulations and on real fMEG recordings, and has a potential to improve the detection of fetal brain signals. The SUBTR removes interference without the need for a model of the individual interference sources. The method may be of interest for any sensor array noise reduction application where signal-free reference channels are available.

Bio-magnetic signatures of fetal breathing movement.

The purpose of fetal magnetoencephalography (fMEG) is to record and analyze fetal brain activity. Unavoidably, these recordings consist of a complex mixture of bio-magnetic signals from both mother and fetus. The acquired data include biological signals that are related to maternal and fetal heart function as well as fetal gross body and breathing movements. Since fetal breathing generates a significant source of bio-magnetic interference during these recordings, the goal of this study was to identify and quantify the signatures pertaining to fetal breathing movements (FBM). The fMEG signals were captured using superconducting quantum interference devices (SQUIDs) The existence of FBM was verified and recorded concurrently by an ultrasound-based video technique. This simultaneous recording is challenging since SQUIDs are extremely sensitive to magnetic signals and highly susceptible to interference from electronic equipment. For each recording, an ultrasound-FBM (UFBM) signal was extracted by tracing the displacement of the boundary defined by the fetal thorax frame by frame. The start of each FBM was identified by using the peak points of the UFBM signal. The bio-magnetic signals associated with FBM were obtained by averaging the bio-magnetic signals time locked to the FBMs. The results showed the existence of a distinctive sinusoidal signal pattern of FBM in fMEG data.

Fetal MEG evoked response latency from beamformer with random field theory.

Analysis of fetal magnetoencephalographic brain recordings is restricted by low signal to noise ratio (SNR) and non-stationarity of the sources. Beamformer techniques have been applied to improve SNR of fetal evoked responses. However, until now the effect of non-stationarity was not taken into account in detail, because the detection of evoked responses is in most cases determined by averaging a large number of trials. We applied a windowing technique to improve the stationarity of the data by using short time segments recorded during a flash-evoked study. In addition, we implemented a random field theory approach for more stringent control of false-positives in the statistical parametric map of the search volume for the beamformer. The search volume was based on detailed individual fetal/maternal biometrics from ultrasound scans and fetal heart localization. Average power over a sliding window within the averaged evoked response against a randomized average background power was used as the test z-statistic. The significance threshold was set at 10% over all members of a contiguous cluster of voxels. There was at least one significant response for 62% of fetal and 95% of newborn recordings with gestational age (GA) between 28 and 45 weeks from 29 subjects. We found that the latency was either substantially unchanged or decreased with increasing GA for most subjects, with a nominal rate of about -11 ms/week. These findings support the anticipated neurophysiological development, provide validation for the beamformer model search as a methodology, and may lead to a clinical test for fetal cognitive development.

Verification of fetal brain responses by coregistration of fetal ultrasound and fetal magnetoencephalography data.

Fetal magnetoencephalography (fMEG) is used to study neurological functions of the developing fetus by measuring magnetic signals generated by electrical sources within the fetal brain. For this aim either auditory or visual stimuli are presented and evoked brain activity or spontaneous activity is measured at the sensor level. However a limiting factor of this approach is the low signal to noise ratio (SNR) of recorded signals. To overcome this limitation, advanced signal processing techniques such as spatial filters (e.g., beamformer) can be used to increase SNR. One crucial aspect of this technique is the forward model and, in general, a simple spherical head model is used. This head model is an integral part of a model search approach to analyze the data due to the lack of exact knowledge about the location of the fetal head. In the present report we overcome this limitation by a coregistration of volumetric ultrasound images with fMEG data. In a first step we validated the ultrasound to fMEG coregistration with a phantom and were able to show that the coregistration error is below 2 cm. In the second step we compared the results gained by the model search approach to the exact location of the fetal head determined on pregnant mothers by ultrasound. The results of this study clearly show that the results of the model search approach are in accordance with the location of the fetal head.

Understanding dynamics of the system using Hilbert phases: an application to study neonatal and fetal brain signals.

The Hilbert phase phi(t) of a signal x(t) exhibits slips when the magnitude of their successive phase difference |phi(t(i+1))-phi(t(i))| exceeds pi. By applying this approach to periodic, uncorrelated, and long-range correlated data, we show that the standard deviation of the time difference between the successive phase slips Deltatau normalized by the percentage of slips in the data is characteristic of the correlation in the data. We consider a 50x50 square lattice and model each lattice point by a second-order autoregressive (AR2) process. Further, we model a subregion of the lattice using a different set of AR2 parameters compared to the rest. By applying the proposed approach to the lattice model, we show that the two distinct parameter regions introduced in the lattice are clearly distinguishable. Finally, we demonstrate the application of this approach to spatiotemporal neonatal and fetal magnetoencephalography signals recorded using 151 superconducting quantum interference device sensors to identify the sensors containing the neonatal and fetal brain signals and discuss the improved performance of this approach over the traditionally used spectral approach.

Magnetomyographic recording and identification of uterine contractions using Hilbert-wavelet transforms.

We propose a multi-stage approach using Wavelet and Hilbert transforms to identify uterine contraction bursts in magnetomyogram (MMG) signals measured using a 151 magnetic sensor array. In the first stage, we decompose the MMG signals by wavelet analysis into multilevel approximate and detail coefficients. In each level, the signals are reconstructed using the detail coefficients followed by the computation of the Hilbert transform. The Hilbert amplitude of the reconstructed signals from different frequency bands (0.1-1 Hz) is summed up over all the sensors to increase the signal-to-noise ratio. Using a novel clustering technique, affinity propagation, the contractile bursts are distinguished from the noise level. The method is applied on simulated MMG data, using a simple stochastic model to determine its robustness and to seven MMG datasets.

An objective assessment of fetal and neonatal auditory evoked responses.

OBJECTIVE: We propose to use cross-correlation function to determine significant fetal and neonatal evoked responses (ERs). METHODS: We quantify ERs by cross-correlation between the stimulus time series and the recorded brain signals. The statistical significance of the correlation is calculated by surrogate analysis. For validation of our approach we investigated a model which mimics the generation of ERs. The model assumes a fixed latency of the ER and contains two parameters, epsilon and lambda. Whether or not the system responds to a given stimulus is controlled by epsilon. The amount to which the system is excited from the base line (background activity) is governed by lambda. We demonstrate the technique by applying it to auditory evoked responses from four fetuses (21 records) between 27 and 39 weeks of gestational age and four neonates (eight records). RESULTS: The method correctly identified the ER and the latency incorporated in the model. A combined analysis of fetuses and neonates data resulted in a significant negative correlation between age and latency. CONCLUSIONS: The analysis of ER, especially for fetal and newborn recordings, should be based on advanced data analysis including the assessment of the significance of responses. The negative correlation between age and latency indicates the neurological maturation. SIGNIFICANCE: The proposed method can be used to objectively assess the ER in fetuses and neonates.

Multiple human papillomavirus types replicate in 3A trophoblasts.

Human papillomavirus (HPV) are more prevalent in spontaneous abortions than elect abortions and preferentially infect the trophoblasts. Related to this, HPV type 16 has been shown to productively replicate in 3A trophoblasts in tissue culture. Extending these earlier studies, the described study addresses the issue whether other genital HPV types (11, 18, and 31) can replicate in trophoblasts. In determining this, HPV-11, 18, or 31 genomic DNAs were lipofected into 3A trophoblasts in culture, thus finding all three HPV types could de novo DNA replicate in 3A trophoblasts (Southern blot) and sequentially express their early and late genes as RNA (RT-PCR) and as protein (immunohistochemistry for L1). HPV-transfected 3A lysates from all three HPV types were also shown to contain HPV infectious units by infection of normal skin raft cultures and by neutralization by specific antibody. Furthermore, microarray analysis revealed the gene expression profile of normal keratinocytes (NK) was closer to 3A trophoblasts than to normal fibroblasts. Moreover, the critical HPV transcription factors AP-1 and Sp1 were found to be more highly expressed in 3A cells than NK. These findings suggest trophoblasts, like squamous epithelium, are broadly permissive for HPV, and some similarities in the gene expression repertoire of these two cell types are consistent with this. Finally, these data support our previous results that demonstrate the relationship between HPV infection of the trophoblast and spontaneous abortions.

Spatial-temporal analysis of fetal bio-magnetic signals.

Non-invasive technique such as magneto-encephalography (MEG), initially pioneered to study human brain signals, has found many other applications in medicine. SQUID(1) Array for Reproductive Assessment (SARA) is a unique non-invasive scanning-device developed at the University of Arkansas for Medical Sciences (UAMS) that can detect fetal brain and other signals. The fetal magneto-encephalography (fMEG) signals often have many bio-magnetic signals mixed in. Examples include the movement of the fetus or muscle contraction of the mother. As a result, the recorded signals may show unexpected patterns, other than the target signal of interest. These "interventions" make it difficult for a physician to assess the exact fetal condition, including its response to various stimuli. We propose using intervention analysis and spatial-temporal auto-regressive moving-average (STARMA) modeling to address the problem. STARMA is a statistical method that examines the relationship between the current observations as a linear combination of past observations as well as observations at neighboring sensors. Through intervention analysis, the change in a pattern due to "interfering" signals can be accounted for. When these interferences are "removed," the end product is a "template" time series, or a typical signal from the target of interest. In this research, a "universal" template is obtained. The template is then used to detect intervention in other datasets by the method of template matching. By this method, it is possible to detect if there is an intervention in any dataset. It will assist physicians in monitoring the actual signal generated by fetal brain and other organs of interest.

Searching for the best model: ambiguity of inverse solutions and application to fetal magnetoencephalography.

Fetal brain signals produce weak magnetic fields at the maternal abdominal surface. In the presence of much stronger interference these weak fetal fields are often nearly indistinguishable from noise. Our initial objective was to validate these weak fetal brain fields by demonstrating that they agree with the electromagnetic model of the fetal brain. The fetal brain model is often not known and we have attempted to fit the data to not only the brain source position, orientation and magnitude, but also to the brain model position. Simulation tests of this extended model search on fetal MEG recordings using dipole fit and beamformers revealed a region of ambiguity. The region of ambiguity consists of a family of models which are not distinguishable in the presence of noise, and which exhibit large and comparable SNR when beamformers are used. Unlike the uncertainty of a dipole fit with known model plus noise, this extended ambiguity region yields nearly identical forward solutions, and is only weakly dependent on noise. The ambiguity region is located in a plane defined by the source position, orientation, and the true model centre, and will have a diameter approximately 0.67 of the modelled fetal head diameter. Existence of the ambiguity region allows us to only state that the fetal brain fields do not contradict the electromagnetic model; we can associate them with a family of models belonging to the ambiguity region, but not with any specific model. In addition to providing a level of confidence in the fetal brain signals, the ambiguity region knowledge in combination with beamformers allows detection of undistorted temporal waveforms with improved signal-to-noise ratio, even though the source position cannot be uniquely determined.

Scaling analysis of paces of fetal breathing, gross-body and extremity movements.

Using detrended fluctuation analysis (DFA), we studied the scaling properties of the time instances (occurrence) of the fetal breathing, gross-body, and extremity movements scored on a second by second basis from the recorded ultrasound measurements of 49 fetuses. The DFA exponent α of all the three movements of the fetuses varied between 0.63 and 1.1. We found an increase in α obtained for the movement due to breathing as a function of the gestational age while this trend was not observed for gross-body and extremity movements. This trend was argued as the indication of the maturation of lung and functional development of respiratory aspect of the fetal central nervous system. This result may be useful in discriminating normal fetuses from high-risk fetuses.

Real-time access of magnetoencephalographic / -cardiographic data: technical realization & application to online fetal heart rate recording.

Current standard magnetoencephalographic and -cardiographic systems do not allow real-time access to the measured data. We developed a software solution for real-time access and used it to create an online fetal heart rate monitor.

Human fetal brain imaging by magnetoencephalography: verification of fetal brain signals by comparison with fetal brain models.

Fetal magnetoencephalogram (fMEG) is measured in the presence of a large interference from maternal and fetal magnetocardiograms (mMCG and fMCG). This cardiac interference can be successfully removed by orthogonal projection of the corresponding spatial vectors. However, orthogonal projection redistributes the fMEG signal among channels. Such redistribution can be readily accounted for in the forward solution, and the signal topography can also be corrected. To assure that the correction has been done properly, and also to verify that the measured signal originates from within the fetal head, we have modeled the observed fMEG by two extreme models where the fetal head is assumed to be either electrically transparent or isolated from the abdominal tissue. Based on the measured spontaneous, sharp wave, and flash-evoked fMEG signals, we have concluded that the model of the electrically isolated fetal head is more appropriate for fMEG analysis. We show with the help of this model that the redistribution due to projection was properly corrected, and also, that the measured fMEG is consistent with the known position of the fetal head. The modeling provides additional confidence that the measured signals indeed originate from within the fetal head.

Time-frequency and coherence analysis of fMEG signals.

Fetal magnetoencephalographic (fMEG) measurements are performed with interference from the fetal and maternal magnetocardiogram (MCG). Fetal movement, fetal breathing, fetal eye blinks or eye rollings and maternal muscle-contraction may generate detectable signals. These factors can be called "interventions," which can be manifested in space and/or time. They make the fMEG signals nonstationary. By examining temporal relationship of the multi-channel records, we are able to find the spatial signature of these "interventions." The aim of this study is to examine nonstationarity in single channel and nonhomogeniety in multiple channels of the fMEG data. Preliminary results are reported here, and may be used in further studies, leading toward intervention identification, and ultimately fetal state determination.