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.

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.

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.

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.

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.

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.

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.

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.

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.

Trophoblasts are the preferential target for human papilloma virus infection in spontaneously aborted products of conception.

In a recent study it has been shown that human papillomavirus (HPV) infection was threefold more prevalent in spontaneous abortion specimens compared with elective specimens (60% [15 of 25] v 20% [3 of 15], respectively) as analyzed by broad-spectrum HPV polymerase chain reaction (PCR) amplification and dot-blot hybridization with an HPV-16 probe. In this study, archival paraffin-embedded tissue from a subset of previously analyzed spontaneous abortion cases were reanalyzed by in situ PCR amplification so that the specific cells infected by HPV could be identified. In the current study, using a new PCR primer set for HPV-16 E6, the status of six previously analyzed cases were verified (five HPV-positive and one negative). Furthermore, syncytiotrophoblasts were identified as the predominant cellular target of HPV (HPV-16 or a related type). Finally, four of four third-trimester placentas similarly analyzed gave no HPV-positive signal. Trophoblasts are the cell type that maintains placental contact with maternal tissue and through which nutrient exchange occurs. This knowledge prompts the hypothesis that HPV-infected trophoblasts may have altered characteristics, which may lead to a compromised gestation.

Sudden joint and extremity pain in pregnancy.

To diagnosis sudden joint and extremity pain in pregnancy, the obstetrician must call upon information from several disciplines, including obstetrics, internal medicine, neurology, and orthopedics. The initial evaluation of the patient should begin with a generalized assessment, and more distant causes of the pain should be ruled out as possible causes are considered, evaluated, and rejected. This article discusses the importance of medical history, physical examination, laboratory studies, synovial fluid analysis, and immunologic studies to aid in the diagnosis of pain, as well as common presentations of musculoskeletal pain in pregnancy.

Application of wavelet transform to uterine electromyographic signals recorded using abdominal surface electrodes.

OBJECTIVE: The aim of this study was to explore the use of the wavelet transform technique to extract and display simultaneously the time, frequency and amplitude information corresponding to electromyographic (EMG) activity of the uterus during labor recorded using abdominal electrodes. METHODS: Uterine EMG signals were recorded from patients in labor using three pairs of electrodes placed across the maternal abdomen. In all the patients uterine activity was also recorded either from an intrauterine pressure catheter (IUPC) or a tocodynamometer. The EMG signals were analyzed using spectral analysis and wavelet analysis. RESULTS: Uterine EMG bursts corresponded with uterine activity measured with either the IUPC or the tocodynamometer. Using wavelet analysis a time-frequency-amplitude plot was obtained to separate out the frequency components relating to uterine EMG activity. CONCLUSION: This study showed that the wavelet transform could be a useful tool to study the uterine EMG activity. Continued studies on frequency content, amplitude and origin of uterine EMG activity could be helpful in understanding uterine contraction.

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.

Spatial-temporal analysis of non-stationary fMEG data.

Magnetoencephalography (MEG) is a technique used to non-invasively record neuromagnetic fields generated by the human brain. Our new SARA (SQUID Array for Reproductive Assessment) is a unique MEG device designed specifically for the study of the fetal neurophysiology. During the acquistion of fetal magnetoencephalography (fMEG), many other interfering bio-magnetic signals are collected as well. Examples include the movement of 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 makes 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 autoregressive 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 sites. Through intervention analysis, the change in pattern due to interfering signals can be well accounted for. When these interferences are removed, the end product is a template time series, or a typical signal from the target of interest thus providing a more reliable means to monitor the actual signals generated by the fetal brain and other organs of interest.

First magnetomyographic recordings of uterine activity with spatial-temporal information using 151 channel sensor array (SARA).

The lack of an effective method for the diagnosis and management of labor points to the need for a new device. SARA-SQUID Array for Reproductive Assessment, is capable of recording spatial-temporal biomagnetic activity. The SARA system is first of its kind in the world dedicated to maternal-fetal research. We non-invasively recorded the magnetomyographic (MMG) signals corresponding to the uterine electrical activity from 7 pregnant mothers. The detailed physiological information obtained simultaneously from 151 sensors spread over the entire abdomen, will help in understanding the origin and propagation of the uterine activity. This information could give us better insight into the mechanism of uterine contraction and may help in better diagnosis and management of labor.

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.