Modeling Magnetomyograms of Uterine Contractions during Pregnancy Using a Multiscale Forward Electromagnetic Approach

Three-dimensional virtual histology of the rat uterus musculature using micro-computed tomography

Contractions of the uterus play an important role in menstruation and fertility, and contractile dysfunction can lead to chronic diseases such as endometriosis. However, the structure and function of the uterus are difficult to interrogate in humans, and thus animal studies are often employed to understand its function. In rats, anatomical studies of the uterus have typically been based on histological assessment, have been limited to small segments of the uterine structure, and have been time-consuming to reconstruct at the organ scale. This study used micro-computed tomography imaging to visualise the muscle structures in the entire non-pregnant rat uterus and assess its use for 3D virtual histology. An assessment of the rodent uterus is presented to (i) quantify muscle thickness variations along the horns, (ii) identify predominant fibre orientations of the muscles and (iii) demonstrate how the anatomy of the uterus can be mapped to 3D volumetric meshes via virtual histology. Micro-computed tomography measurements were validated against measurements from histological sections. The average thickness of the myometrium was found to be 0.33 ± 0.11 mm and 0.31 ± 0.09 mm in the left and right horns, respectively. The micro-computed tomography and histology thickness calculations were found to correlate strongly at different locations in the uterus: at the cervix, r = 0.87, and along the horn from the cervical end to the ovarian end, respectively, r = 0.77, r = 0.89 and r = 0.54, with p < 0.001 in every location. This study shows that micro-computed tomography can be used to quantify the musculature in the whole non-pregnant uterus and can be used for 3D virtual histology.

Parametric Solid Models of the At-Term Uterus From Magnetic Resonance Images

Birthing mechanics are poorly understood, though many injuries during childbirth are mechanical, like fetal and maternal tissue damage. Several biomechanical simulation models of parturition have been proposed to investigate birth, but many do not include the uterus. Additionally, most solid models rely on segmenting anatomical structures from clinical images to generate patient geometry, which can be time-consuming. This work presents two new parametric solid modeling methods for generating patient-specific, at-term uterine three-dimensional geometry. Building from an established method of modeling the sagittal uterine shape, this work improves the uterine coronal shape, especially where the fetal head joins the lower uterine wall. Solid models of the uterus and cervix were built from five at-term patients' magnetic resonance imaging (MRI) sets. Using anatomy measurements from MRI-segmented models, two parametric models were created-one that employs an averaged coronal uterine shape and one with multiple axial measurements of the coronal uterus. Through finite element analysis, the two new parametric methods were compared to the MRI-segmented high-fidelity method and a previously published elliptical low-fidelity method. A clear improvement in the at-term uterine shape was found using the two new parametric methods, and agreement in principal Lagrange strain directions was observed across all modeling methods. These methods provide an effective and efficient way to generate three-dimensional solid models of patient-specific maternal uterine anatomy, advancing possibilities for future research in computational birthing biomechanics.

Predicting preterm births from electrohysterogram recordings via deep learning

About one in ten babies is born preterm, i.e., before completing 37 weeks of gestation, which can result in permanent neurologic deficit and is a leading cause of child mortality. Although imminent preterm labor can be detected, predicting preterm births more than one week in advance remains elusive. Here, we develop a deep learning method to predict preterm births directly from electrohysterogram (EHG) measurements of pregnant mothers recorded at around 31 weeks of gestation. We developed a prediction model, which includes a recurrent neural network, to predict preterm births using short-time Fourier transforms of EHG recordings and clinical information from two public datasets. We predicted preterm births with an area under the receiver-operating characteristic curve (AUC) of 0.78 (95% confidence interval: 0.76-0.80). Moreover, we found that the spectral patterns of the measurements were more predictive than the temporal patterns, suggesting that preterm births can be predicted from short EHG recordings in an automated process. We show that preterm births can be predicted for pregnant mothers around their 31st week of gestation, prompting beneficial treatments to reduce the incidence of preterm births and improve their outcomes.

Modeling and experimental approaches for elucidating multi-scale uterine smooth muscle electro- and mechano-physiology: A review

The uterus provides protection and nourishment (via its blood supply) to a developing fetus, and contracts to deliver the baby at an appropriate time, thereby having a critical contribution to the life of every human. However, despite this vital role, it is an under-investigated organ, and gaps remain in our understanding of how contractions are initiated or coordinated. The uterus is a smooth muscle organ that undergoes variations in its contractile function in response to hormonal fluctuations, the extreme instance of this being during pregnancy and labor. Researchers typically use various approaches to studying this organ, such as experiments on uterine muscle cells, tissue samples, or the intact organ, or the employment of mathematical models to simulate the electrical, mechanical and ionic activity. The complexity exhibited in the coordinated contractions of the uterus remains a challenge to understand, requiring coordinated solutions from different research fields. This review investigates differences in the underlying physiology between human and common animal models utilized in experiments, and the experimental interventions and computational models used to assess uterine function. We look to a future of hybrid experimental interventions and modeling techniques that could be employed to improve the understanding of the mechanisms enabling the healthy function of the uterus.

Review and Study of Uterine Bioelectrical Waveforms and Vector Analysis to Identify Electrical and Mechanosensitive Transduction Control Mechanisms During Labor in Pregnant Patients

The bioelectrical signals that produce uterine contractions during parturition are not completely understood. The objectives are as follows: (1) to review the literature and information concerning uterine biopotential waveforms generated by the uterus, known to produce contractions, and evaluate mechanotransduction in pregnant patients using electromyographic (EMG) recording methods and (2) to study a new approach, uterine vector analysis, commonly used for the heart: vectorcardiography analysis. The patients used in this study were as follows: (1) patients at term not in labor (n = 3); (2) patients during the 1st stage of labor at cervical dilations from 2 to 10 cm (n = 30); and (3) patients in the 2nd stage of labor and during delivery (n = 3). We used DC-coupled electrodes and PowerLab hardware (model no. PL2604, ADInstruments, Castle Hill, Australia), with software (LabChart, ADInstruments) for storage and analysis of biopotentials. Uterine and abdominal EMG recordings were made from the surface of each patient using 3 electrode pairs with 1 pair (+ and -, with a 31-cm spacing distance) placed in the right/left position (X position) and with 1 pair placed in an up/down position (Y position, also 31 cm apart) and with the third pair at the front/back (Z position). Using signals from the three X, Y, and Z electrodes, slow (0.03 to 0.1 Hz, high amplitude) and fast wave (0.3 to 1 Hz, low amplitude) biopotentials were recorded. The amplitudes of the slow waves and fast waves were significantly higher during the 2nd stage of labor compared to the 1st stage (respectively, p = 9.54 × e^-3 and p = 3.94 × e^-7). When 2 channels were used, for example, the X vs. Y, for 2-D vector analysis or 3 channels, X vs. Y vs. Z, for 3-D analysis, are plotted against each other on their axes, this produces a vector electromyometriogram (EMMG) that shows no directionality for fast waves and a downward direction for slow waves. Similarly, during the 2nd stage of labor during abdominal contractions ("pushing"), the slow and fast waves were enlarged. Manual applied pressure was used to evoke bioelectrical activity to examine the mechanosensitivity of the uterus. Conclusions: (1) Phasic contractility of the uterus is a product of slow waves and groups of fast waves (bursts of spikes) to produce myometrial contractile responses. (2) 2-D and 3-D uterine vector analyses (uterine vector electromyometriogram) demonstrate no directionality of small fast waves while the larger slow waves represent the downward direction of biopotentials towards the cervical opening. (3) Myometrial cell action event excitability and subsequent contractility likely amplify slow wave activity input and uterine muscle contractility via mechanotransduction systems. (4) Models illustrate the possible relationships of slow to fast waves and the association of a mechanotransduction system and pacemaker activity as observed for slow waves and pacemakers in gastrointestinal muscle. (5) The interaction of these systems is thought to regulate uterine contractility. (6) This study suggests a potential indicator of delivery time. Such vector approaches might help us predict the progress of gestation and better estimate the timing of delivery, gestational pathologies reflected in bioelectric events, and perhaps the potential for premature delivery drug and mechanical interventions.

Spatial-dependent regularization to solve the inverse problem in electromyometrial imaging

Recently, electromyometrial imaging (EMMI) was developed to non-invasively image uterine contractions in three dimensions. EMMI collects body surface electromyography (EMG) measurements and uses patient-specific body-uterus geometry generated from magnetic resonance images to reconstruct uterine electrical activity. Currently, EMMI uses the zero-order Tikhonov method with mean composite residual and smoothing operator (CRESO) to stabilize the underlying ill-posed inverse computation. However, this method is empirical and implements a global regularization parameter over all uterine sites, which is sub-optimal for EMMI given the severe eccentricity of body-uterus geometry. To address this limitation, we developed a spatial-dependent (SP) regularization method that considers both body-uterus eccentricity and EMG noise. We used electrical signals simulated with spherical and realistic geometry models to compare the reconstruction accuracy of the SP method to those of the CRESO and the L-Curve methods. The SP method reconstructed electrograms and potential maps more accurately than the other methods, especially in cases of high eccentricity and noise contamination. Thus, the SP method should facilitate clinical use of EMMI and can be used to improve the accuracy of other electrical imaging modalities, such as Electrocardiographic Imaging. Graphical abstract The spatial-dependent regularization (SP) technique was designed to improve the accuracy of Electromyometrial Imaging (EMMI). The top panel shows the eccentricity of body-uterus geometry and four representative body surface electrograms. The bottom panel shows boxplots of correlation coefficients and relative errors for the electrograms reconstructed with SP and two conventional methods, the L-Curve and mean CRESO methods.

Accuracy of electromyometrial imaging of uterine contractions in clinical environment

Clinically, uterine contractions are monitored with tocodynamometers or intrauterine pressure catheters. In the research setting, electromyography (EMG), which detects electrical activity of the uterus from a few electrodes on the abdomen, is feasible, can provide more accurate data than these other methods, and may be useful for predicting preterm birth. However, EMG lacks sufficient spatial resolution and coverage to reveal where uterine contractions originate, how they propagate, and whether preterm contractions differ between women who do and do not progress to preterm delivery. To address those limitations, electromyometrial imaging (EMMI) was recently developed and validated to non-invasively assess three-dimensional (3D) electrical activation patterns on the entire uterine surface in pregnant sheep. EMMI uses magnetic resonance imaging to obtain subject-specific body-uterus geometry and collects uterine EMG data from up to 256 electrodes on the body surface. EMMI software then solves an ill-posed inverse computation to combine the two datasets and generate maps of electrical activity on the entire 3D uterine surface. Here, we assessed the feasibility to clinically translate EMMI by evaluating EMMI's accuracy under the unavoidable geometrical alterations and electrical noise contamination in a clinical environment. We developed a hybrid experimental-simulation platform to model the effects of fetal kicks, contractions, fetal/maternal movements, and noise contamination caused by maternal respiration and environmental electrical activity. Our data indicate that EMMI can accurately image uterine electrical activity in the presence of geometrical deformations and electrical noise, suggesting that EMMI can be reliably translated to non-invasively image 3D uterine electrical activation in pregnant women.

Electro-Mechanical Ionic Channel Modeling for Uterine Contractions and Oxytocin Effect during Pregnancy

Uterine contractions during normal pregnancy and preterm birth are an important physiological activity. Although the cause of preterm labor is usually unknown, preterm birth creates very serious health concerns in many cases. Therefore, understanding normal birth and predicting preterm birth can help both newborn babies and their families. In our previous work, we developed a multiscale dynamic electrophysiology model of uterine contractions. In this paper, we mainly focus on the cellular level and use electromyography (EMG) and cell force generation methods to construct a new ionic channel model and a corresponding mechanical force model. Specifically, the ionic channel model takes into consideration the knowledge of individual ionic channels, which include the electrochemical and bioelectrical characteristics of individual myocytes. We develop a new sodium channel and a new potassium channel based on the experimental data from the human myometrium and the average correlations are 0.9946 and 0.9945, respectively. The model is able to generate the single spike, plateau type and bursting type of action potentials. Moreover, we incorporate the effect of oxytocin on changing the properties of the L-type and T-type calcium channels and further influencing the output action potentials. In addition, we develop a mechanical force model based on the new ionic channel model that describes the detailed ionic dynamics. Our model produces cellular mechanical force that propagates to the tissue level. We illustrate the relationship between the cellular mechanical force and the intracellular ionic dynamics and discuss the relationship between the application of oxytocin and the output mechanical force. We also propose a simplified version of the model to enable large scale simulations using sensitivity analysis method. Our results show that the model is able to reproduce the bioelectrical and electromechanical characteristics of uterine contractions during pregnancy.

Connectivity Measures of Uterine Activity using Magnetomyography

This work explores the use of graph-theoretical metrics of network topography to investigate interactions in the uterine activity using a multi-channel SQUID array. Magnetomyography (MMG) is a noninvasive technique that records magnetic fields associated with the uterine activity. Graph analysis was applied to 30s no-overlap epochs of MMG data for evaluating the evolution of local and global connectivity, and centrality indicators within the network. Binary graphs were obtained by applying a range of thresholds from 10% to 35% of the strongest edges preserved. Network analysis was applied to 24 simulated MMG data when independent noise realizations were added. Simulated data was generated from a multiscale forward model that uses a realistic uterus representation. Additionally, we applied network analysis to repeated real MMG measurements obtained from a subject at different gestational ages (GA) to observe the evolution of the network until subject reaches active labor. Results show in the simulation setting that network metrics were higher during the burst activity reflecting the propagation activity of the signal across the uterus of the multiscale mathematical model. The local efficiency values were higher than the global efficiency for any threshold used. For real MMG recordings, global and local efficiency, and clustering coefficient values increased as the patient approached active labor at any binarized threshold whereas betweenness centrality quantity decreased with days to active labor.

Estimating uterine source current during contractions using magnetomyography measurements

Understanding the uterine source of the electrophysiological activity of contractions during pregnancy is of scientific interest and potential clinical applications. In this work, we propose a method to estimate uterine source currents from magnetomyography (MMG) temporal course measurements on the abdominal surface. In particular, we develop a linear forward model, based on the quasistatic Maxwell’s equations and a realistic four-compartment volume conductor, relating the magnetic fields to the source currents on the uterine surface through a lead-field matrix. To compute the lead-field matrix, we use a finite element method that considers the anisotropic property of the myometrium. We estimate the source currents by minimizing a constrained least-squares problem to solve the non-uniqueness issue of the inverse problem. Because we lack the ground truth of the source current, we propose to predict the intrauterine pressure from our estimated source currents by using an absolute-value-based method and compare the result with real abdominal deflection recorded during contractile activity. We test the feasibility of the lead-field matrix by displaying the lead fields that are generated by putative source currents at different locations in the myometrium: cervix and fundus, left and right, front and back. We then illustrate our method by using three synthetic MMG data sets, which are generated using our previously developed multiscale model of uterine contractions, and three real MMG data sets, one of which has simultaneous real abdominal deflection measurements. The numerical results demonstrate the ability of our method to capture the local contractile activity of human uterus during pregnancy. Moreover, the predicted intrauterine pressure is in fair agreement with the real abdominal deflection with respect to the timing of uterine contractions.

Estimation of preterm labor immediacy by nonlinear methods

Preterm delivery affects about one tenth of human births and is associated with an increased perinatal morbimortality as well as with remarkable costs. Even if there are a number of predictors and markers of preterm delivery, none of them has a high accuracy. In order to find quantitative indicators of the immediacy of labor, 142 cardiotocographies (CTG) recorded from women consulting because of suspected threatened premature delivery with gestational ages comprehended between 24 and 35 weeks were collected and analyzed. These 142 samples were divided into two groups: the delayed labor group (n = 75), formed by the women who delivered more than seven days after the tocography was performed, and the anticipated labor group (n = 67), which corresponded to the women whose labor took place during the seven days following the recording. As a means of finding significant differences between the two groups, some key informational properties were analyzed by applying nonlinear techniques on the tocography recordings. Both the regularity and the persistence levels of the delayed labor group, which were measured by Approximate Entropy (ApEn) and Generalized Hurst Exponent (GHE) respectively, were found to be significantly different from the anticipated labor group. As delivery approached, the values of ApEn tended to increase while the values of GHE tended to decrease, suggesting that these two methods are sensitive to labor immediacy. On this paper, for the first time, we have been able to estimate childbirth immediacy by applying nonlinear methods on tocographies. We propose the use of the techniques herein described as new quantitative diagnosis tools for premature birth that significantly improve the current protocols for preterm labor prediction worldwide.