Jeremy Laforet

Toward a Multiscale Model of the Uterine Electrical Activity

A comprehensive multiscale model of the uterine muscle electrical activity would permit understanding the important link between the genesis and evolution of the action potential at the cell level and the process leading to labor. Understanding this link can open the way to more effective tools for the prediction of labor and prevention of preterm delivery. A first step toward the realization of such a model is presented here. By using as starting point a previously published model of the generation of the uterine muscle action potential at the cell level, a significant reduction of the model complexity is here achieved in order to simulate 2-D propagation of the cellular activity at the uterine tissue level, for tissue strips of arbitrary dimension. From the obtained dynamic behavior of the electrical activity simulated at the tissue level, the use of a previously validated volume conductor model at the organ level permits us to simulate the electrohysterogram as recorded on the abdominal surface by an electrode array. Qualitative evaluation of the model at the cell level and at the organ level confirms the potential of the proposed multiscale approach for further refinement and extension aiming at clinical application.

Fast generation model of high density surface EMG signals in a cylindrical conductor volume

In the course of the last decade, fast and qualitative computing power developments have undoubtedly permitted for a better and more realistic modeling of complex physiological processes. Due to this favorable environment, a fast, generic and reliable model for high density surface electromyographic (HD-sEMG) signal generation with a multilayered cylindrical description of the volume conductor is presented in this study. Its main peculiarity lies in the generation of a high resolution potential map over the skin related to active Motor Units (MUs). Indeed, the analytical calculus is fully performed in the frequency domain. HD-sEMG signals are obtained by surfacic numerical integration of the generated high resolution potential map following a variety of electrode shapes. The suggested model is implemented using parallel computing techniques as well as by using an object-oriented approach which is comprehensive enough to be fairly quickly understood, used and potentially upgraded. To illustrate the model abilities, several simulation analyses are put forward in the results section. These simulations have been performed on the same muscle anatomy while varying the number of processes in order to show significant speed improvement. Accuracy of the numerical integration method, illustrating electrode shape diversity, is also investigated in comparison to analytical transfer functions definition. An additional section provides an insight on the volume detection of a circular electrode according to its radius. Furthermore, a large scale simulation is introduced with 300MUs in the muscle and a HD-sEMG electrode grid composed of 16×16 electrodes for three constant isometric contractions in 12s. Finally, advantages and limitations of the proposed model are discussed with a focus on perspective works.

An electro-mechanical multiscale model of uterine pregnancy contraction

Detecting preterm labor as early as possible is important because tocolytic drugs are much more likely to delay preterm delivery if administered early. Having good information on the real risk of premature labor also leads to fewer women who do not need aggressive treatment for premature labor threat. Currently, one of the most promising ways to diagnose preterm labor threat is the analysis of the electrohysterogram (EHG). Its characteristics have been related to preterm labor risk but they have not proven to be sufficiently accurate to use in clinical routine. One of the reasons for this is that the physiology of the pregnant uterus is insufficiently understood. Models already exist in literature that simulate either the electrical or the mechanical component of the uterine smooth muscle. Few include both components in a co-simulation of electrical and mechanical aspects. A model that can represent realistically both the electrical and the mechanical behavior of the uterine muscle could be useful for better understanding the EHG and therefore for preterm labor detection. Processing the EHG considers only the electrical component of the uterus but the electrical activity does not seem to explain by itself the synchronization of the uterine muscle that occurs during labor and not at other times. Recent studies have demonstrated that the mechanical behavior of the uterine muscle seems to play an important role in uterus synchronization during labor. The aim of the proposed study is to link three different models of the uterine smooth muscle behavior by using co-simulation. The models go from the electrical activity generated at the cellular level to the mechanical force generated by the muscle and from there to the deformation of the tissue. The results show the feasibility of combining these three models to model a whole uterus contraction on 3D realistic uterus model.

Muscle force estimation using data fusion from high-density SEMG grid

The aim of the proposed work is to evaluate, by simulation, the introduction of a data fusion process from a HD-sEMG grid (8×8) to improve the muscle force estimation from sEMG signal. For this purpose, twelve electrode arrangements are combined to dimension reduction technique (PCA or channel averaging) to obtain a monodimensional sEMG signal. After, this signal is used in a sEMG-force relationship model to estimate the muscular force. In fact, two models, with different complexity, and used in the biomechanics community are studied. In the simulation, three isometric contractions are simulated (20%, 50% and 80% MVC) using a recent sEMG-force generation model. Finally, the Normalized RMS Difference (NRMSD) between the estimated force and the simulated force by the sEMG-force generation model is calculated for each combination (electrode arrangement and dimension reduction technique, force estimator). According to the obtained results, the combination PCA and Laplacian arrangement gave the best fitting using the second force estimator while the best result obtained for the first force estimator is with the Right Diagonal Bipolar (DBR) arrangement combined with channel averaging. In future works, these force estimators, combined to HD-sEMG data fusion, will be experimentally evaluated.

Smooth muscle modeling and experimental identification: application to bladder isometric contraction.

This paper presents an original smooth muscle model based on the Huxley microscopic approach. This model is the main part of a comprehensive lower urinary track model. The latter is used for simulation studies and is assessed through experiments on rabbits, for which a subset of parameters is estimated, using intravesical pressure measurements in isometric conditions. Bladder contraction is induced by electrical stimulation that determines the onset and thus synchronizes simulation and experimental data. Model sensitivity versus parameter accuracy is discussed and allows the definition of a subset of four parameters that must be accurately identified in order to obtain good fitting between experimental and acquired data. Preliminary experimental data are presented as well as model identification results. They show that the model is able to follow the pressure changes induced by an artificial stimulus in isometric contractions. Moreover, the model gives an insight into the internal changes in calcium concentration and the ratio of the different chemical species present in the muscle cells, in particular the bounded and unbounded actin and myosin and the normalized concentration of intracellular calcium.

Estimation of Coupling and Directionality between Signals Applied to Physiological Uterine EMG Model and Real EHG Signals

Several measures have been proposed to detect the strength and direction of relationships between biosignals. In this paper we study two nonlinear methods that are widely used in functional and effective connectivity analysis: nonlinear correlation coefficient (h 2) and general synchronization (H). The performance of both methods is tested on two dimensional coupled synthetic nonlinear Rossler model. The best method for these synthetic Rossler signals is then applied for the first time to signals generated by a physiological uterine EMG model. It was then applied to real uterine EMG recorded during pregnancy and labor in order to compare their synchronization and propagation direction. The best performing method was tested on the physiological model data, and the results obtained on real signals are encouraging. It may possibly be of use for solving the open questions of the relationships between uterine EMG at different places on the uterus, and may provide a way to localize their sources.

Analysis of the sEMG/force relationship using HD-sEMG technique and data fusion

The relationship between the surface Electromyogram (sEMG) signal and the force of an individual muscle is still ambiguous due to the complexity of experimental evaluation. However, understanding this relationship should be useful for the assessment of neuromuscular system in healthy and pathological contexts. In this study, we present a global investigation of the factors governing the shape of this relationship. Accordingly, we conducted a focused sensitivity analysis of the sEMG/force relationship form with respect to neural, functional and physiological parameters variation. For this purpose, we used a fast generation cylindrical model for the simulation of an 8×8 High Density-sEMG (HD-sEMG) grid and a twitch based force model for the muscle force generation. The HD-sEMG signals as well as the corresponding force signals were simulated in isometric non-fatiguing conditions and were based on the Biceps Brachii (BB) muscle properties. A total of 10 isometric constant contractions of 5s were simulated for each configuration of parameters. The Root Mean Squared (RMS) value was computed in order to quantify the sEMG amplitude. Then, an image segmentation method was used for data fusion of the 8×8 RMS maps. In addition, a comparative study between recent modeling propositions and the model proposed in this study is presented. The evaluation was made by computing the Normalized Root Mean Squared Error (NRMSE) of their fitting to the simulated relationship functions. Our results indicated that the relationship between the RMS (mV) and muscle force (N) can be modeled using a 3rd degree polynomial equation. Moreover, it appears that the obtained coefficients are patient-specific and dependent on physiological, anatomical and neural parameters.

Realistic motor unit placement in a cylindrical HD-sEMG generation model

The aim of this work is to assess an automatic optimized algorithm for the positioning of the Motor Units (MUs) within a multilayered cylindrical High Density surface EMG (HD-sEMG) generation model representing a skeletal muscle. The multilayered cylinder is composed of three layers: muscle, adipose and skin tissues. For this purpose, two different algorithms will be compared: an unconstrained random and a Mitchells Best Candidate (MBC) placements, both with uniform distribution for the MUs positions. These algorithms will then be compared by their fiber density within the muscle and by using a classical amplitude descriptor, the Root-Mean-Square (RMS) amplitude value obtained from 64 HD-sEMG signals recorded by an 8×8 electrode grid of circular electrode during one contraction at 70% Maximum Voluntary Contraction (MVC) in both simulation and experimental conditions for the Biceps Brachii (BB) muscle. The obtained results clearly exposed the necessity to use a specific algorithm to place the MUs within the muscle representation volume in agreement with physiology.

Sensitivity Analysis of HD-sEMG Amplitude Descriptors Relative to Grid Parameter Variation

The aim of this work is to perform a sensitivity analysis of a high density surface electromyogram (HD-sEMG) amplitude descriptors according to several grid parameters. For this purpose, an analytical limb model is used, where the upper limb is modeled as a multilayered cylinder with three layers: muscle, fat tissue and skin tissue. Using this model, HD- sEMG signals are computed over the skin as a 2D surface along angular and longitudinal directions. Electrode recording is performed through a surface integration on the 2D surface according to the electrode shape. 3 simulations with the same anatomy (350 Motor Units) were computed for 3 constant contraction levels: 30%, 50% and 70% of the Maximal Voluntary Contraction (MVC). Then, a global sensitivity analysis using Morris formalism is performed to explore the sensitivity of amplitude descriptors (ARV, RMS and HOS) relative to vary parameters from the electrode grid (inter-electrode distances, electrodes radius, position and rotation). The obtained results clearly exposed a huge impact of the grid rotation on the studied criteria. They also showed that parameters specific to the electrode grid layout (inter-electrode distances) have the less impact.

A multiscale model of the electrohysterogram the BioModUE_PTL project

The electrohysterogram (EHG) is a promising means of monitoring pregnancy and of detecting a risk of preterm labor. To improve our understanding of the EHG as well as its relationship with the physiologic phenomena involved in uterine contractility, we plan to model these phenomena in terms of generation and propagation of uterine electrical activity. This activity can be realistically modeled by representing the principal ionic dynamics at the cell level, the propagation of electrical activity at the tissue level and then the way it is reflected on the skin surface through the intervening tissue. We present in this paper the different steps leading to the development and validation of a biophysics based multiscale model of the EHG, going from the cell to the electrical signal measured on the abdomen.