Electrohysterographic Signals Research Articles

Automated electrohysterographic detection of uterine contractions for monitoring of pregnancy: feasibility and prospects

BackgroundPreterm birth is a major public health problem in developed countries. In this context, we have conducted research into outpatient monitoring of uterine electrical activity in women at risk of preterm delivery. The objective of this preliminary study was to perform automated detection of uterine contractions (without human intervention or tocographic signal, TOCO) by processing the EHG recorded on the abdomen of pregnant women. The feasibility and accuracy of uterine contraction detection based on EHG processing were tested and compared to expert decision using external tocodynamometry (TOCO) .MethodsThe study protocol was approved by local Ethics Committees under numbers ID-RCB 2016-A00663-48 for France and VSN 02-0006-V2 for Iceland.Two populations of women were included (threatened preterm birth and labour) in order to test our system of recognition of the various types of uterine contractions.EHG signal acquisition was performed according to a standardized protocol to ensure optimal reproducibility of EHG recordings. A system of 18 Ag/AgCl surface electrodes was used by placing 16 recording electrodes between the woman’s pubis and umbilicus according to a 4 × 4 matrix. TOCO was recorded simultaneously with EHG recording.EHG signals were analysed in real-time by calculation of the nonlinear correlation coefficient H2. A curve representing the number of correlated pairs of signals according to the value of H2 calculated between bipolar signals was then plotted. High values of H2 indicated the presence of an event that may correspond to a contraction.Two tests were performed after detection of an event (fusion and elimination of certain events) in order to increase the contraction detection rate.ResultsThe EHG database contained 51 recordings from pregnant women, with a total of 501 contractions previously labelled by analysis of the corresponding tocographic recording. The percentage recognitions obtained by application of the method based on coefficient H2 was 100% with 782% of false alarms. Addition of fusion and elimination tests to the previously obtained detections allowed the false alarm rate to be divided by 8.5, while maintaining an excellent detection rate (96%).ConclusionThese preliminary results appear to be encouraging for monitoring of uterine contractions by algorithm-based automated detection to process the electrohysterographic signal (EHG). This compact recording system, based on the use of surface electrodes attached to the skin, appears to be particularly suitable for outpatient monitoring of uterine contractions, possibly at home, allowing telemonitoring of pregnancies. One of the advantages of EHG processing is that useful information concerning contraction efficiency can be extracted from this signal, which is not possible with the TOCO signal.

NEW AR/ARMA MODEL SELECTION METHOD FOR FINITE LENGTH TIME SERIES WITH APPLICATION TO PREDICTION OF PRETERM DELIVERIES

In this paper, we proposed a new method able to select, simultaneously, from finite length time-series sequence, the suitable model type; pure Autoregressive (AR) or Autoregressive Moving Average (ARMA), which best describes the process that generated data. This is also a new way for estimating ARMA model order. The proposed method is different in its concept from all other methods found in the literature, direct and far simpler to implement. We proposed new empirical criteria basing on specific AR order selection criteria for finite length time series. We tested the proposed criteria on 10000 realizations of different broadband and narrow band processes. We also studied effects of additive noise and data length on their performance. The best mean recognition rate of model type was 92%. [Formula: see text]-value of Fisher’s exact test was always strictly under [Formula: see text]. Power spectral densities of selected models managed to recover the true Power spectral density (PSDs) shapes by resolving close spectral peaks and by finding their positions and their bandwidths with negligible spectral biases. Efficiency of our method was proved by its application in automatic segmentation of electrohysterographic signals (EHG) in the aim of predicting preterm deliveries with a mean rate of 90%.

Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records.

In this study, we proposed an approach able to predict whether a pregnant woman with contractions would give birth earlier than expected (i.e., before the 37 t h week of gestation (WG)). It only processes non-invasive electrohysterographic (EHG) signals fully automatically without assistance of an expert or an additional medical system. We used term and preterm EHG signals of 30-minutes duration collected between the 27 t h and the 32 n d WG. Preterm deliveries (< 37W G) had occurred in average 4.00 ± 1.88 weeks since recording dates. Each recording contains three bipolar channels. Using the Huang-Hilbert transform (HHT), we obtained up to twelve intrinsic mode functions (IMFs) for each signal. We found that the most relevant IMFs for both term and preterm records were IMF3 and IMF6. From these two IMFs, we extracted 8 most relevant features targeting EHG signal specificities. We investigated features classifications using support vector machine (SVM) for the 3 single-channels and for all their possible combinations. High discrimination power between term and preterm EHG records was obtained with linear-SVM classifiers. For almost all the cases, mean areas under curves (AUC) exceeded 0.92. A two-channel combination (7 features) achieved the best mean results with A c c u r a c y = 95.70%, S e n s i t i v i t y = 98.40%, S p e c i f i c i t y = 93.00% and A U C = 0.95. Results of the three-channel combination (9 features) were A c c u r a c y = 92.30%, S e n s i t i v i t y = 93.00%, S p e c i f i c i t y = 91.60% and A U C = 0.96. The best single-channel (8 features) gave the mean values: A c c u r a c y = 90.40%, S e n s i t i v i t y = 93.60% and A U C = 0.94. Thus, the advantage of our approach is the high diagnostic performance at low computational cost.

Identification of preterm birth based on RQA analysis of electrohysterograms

Background and objectiveCommon methods for data analysis are mainly based on linear concepts, but in recent years nonlinear dynamics methods have been introduced. It is a well-known fact that In typical biological systems lack of stationarity and rather sudden changes of state are the properties distinguishing them from each other. There is an urgent need to better understand the mechanical activity of the myometrium (its contractility) to find a solution for preterm delivery problem, the largest cause of neonatal deaths and morbidity. The electrohysterographic signal (EHG) is a good non-linear, bioelectrical indicator for the detection and identification of term and preterm birth. MethodsThe material of the study consists of EHG signals, obtained from 20 patients between the 24th and the 28th week of pregnancy with threatened preterm labor. The women were divided into two groups: those delivering after more than 7 days - group A (n = 10) and women delivering within 7 days - group B (n = 10). In this paper, an analysis of bioelectrical signals was performed by recurrence quantification analysis (RQA) and principal component analysis (PCA) to distinguish particular patterns for term and preterm birth. To date, these methods have not been used for the evaluation of bioelectrical activity in the uterus. To train novel classifiers for the EHG signals Support Vectors Machine classifications (multiclass SVM) was used. Statistical analysis was performed by means of non-parametric Mann-Whitney test. ResultsFrom among eleven parameters obtained from recurrence quantification analysis, five most appropriate were chosen: Recurrence Rate, Determinism, Laminarity, Entropy and Recurrence Period Density Entropy. Significant increase (p < .001) of Recurrence Rate was found in patients from group B, while increase of parameters, besides Laminarity, was found in patients from group A. The accuracy of classification obtained as a result of the analysis increased to 83,32%. ConclusionWe showed that the respectively selected recurrence quantificators obtained for that time series could be used to classify all those signals to the appropriate group. The proposed analysis could help in detecting preterm labor based on the EHG signal dynamics.

Effect of uterine contractions on fetal heart rate in pregnancy: a prospective observational study.

The new Holter monitoring technology enables long-term electrocardiographic recording of the fetal heart rate without discomfort for the mother. The aim of the study was to assess the feasibility of a fetal Holter monitor. This technology was further used to study fetal heart rate outside the hospital setting during normal daily activities and to test the hypothesis that uterine activity during pregnancy influences fetal heart rate. Prospective observational study including 12 healthy pregnant women at 20-40weeks of gestation. Data were collected using the Monica AN24 system. Outcome measures were fetal heart rate, maternal heart rate, and uterine activity categorized according to the strength of the electrohysterographic signal. The recordings had a median length of 18.8h, and fetal heart rate and maternal heart rate were obtained with success rates of 73.1 and 99.9%, respectively. Uterine activity was found to affect fetal heart rate in all participants. Compared with the basal tone and mild levels of uterine activity, moderate and strong levels of uterine activity were associated with increases in fetal heart rate of 4.0 and 5.7beats/min, respectively. At night, the corresponding increases were 4.9 and 7.6beats/min. Linear correlations were found between maternal heart rate and fetal heart rate in 11 of the 12 cases, with a mean coefficient beta of 0.189. Both maternal heart rate and fetal heart rate exhibitedadiurnal pattern, with lower heart rates being recorded at night. Uterine activity during pregnancy is associated with a graded response in fetal heart rate and may represent a physiological challenge for the development and adaptation of the fetal cardiovascular system.

A node-wise analysis of the uterine muscle networks for pregnancy monitoring.

The recent past years have seen a noticeable increase of interest in the correlation analysis of electrohysterographic (EHG) signals in the perspective of improving the pregnancy monitoring. Here we propose a new approach based on the functional connectivity between multichannel (4×4 matrix) EHG signals recorded from the women's abdomen. The proposed pipeline includes i) the computation of the statistical couplings between the multichannel EHG signals, ii) the characterization of the connectivity matrices, computed by using the imaginary part of the coherence, based on the graph-theory analysis and iii) the use of these measures for pregnancy monitoring. The method was evaluated on a dataset of EHGs, in order to track the correlation between EHGs collected by each electrode of the matrix (called `node-wise' analysis) and follow their evolution along weeks before labor. Results showed that the strength of each node significantly increases from pregnancy to labor. Electrodes located on the median vertical axis of the uterus seemed to be the more discriminant. We speculate that the network-based analysis can be a very promising tool to improve pregnancy monitoring.

Early diagnosis of threatened premature labor by electrohysterographic recordings – The use of digital signal processing

Prevention and early diagnosis of imminent preterm labor are considered to be the most important perinatal challenge nowadays. Significant progress has been observed on postnatal care of premature infants, but without reducing the prevalence of preterm delivery.Our study was focused on comparison of three methods of spectral analysis of electrohysterographic (EHG) signals: fast Fourier transform (FFT), wavelet transform (WT) and autoregressive modeling (AR). Complexity of the electrohysterographic signals was analyzed by using: the approximate entropy (ApEn), Lempel–Ziv complexity measure (L–Z). Additionally, the work evaluated the applicability of EHG in diagnosing imminent premature labor.EHG signals were recorded among 60 patients with threatened preterm labor symptoms between the 24th and 34th week of pregnancy. Patients included to the study had a shortened cervix (less than 20mm) without regular uterine contractions recorded on regular cardiotocography (CTG). The women were divided into two groups: those delivering within 7 days – group A (n=15) and women delivering after 7 days – group B (n=45).The study confirmed differences in bioelectrical activity of uterus between patients delivering prematurely within 7 days and after from the EHG registration for all analyzed methods.

Advanced artificial neural network classification for detecting preterm births using EHG records

Globally, the rate of preterm births are increasing, thus resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. Nevertheless, there has been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect the onset of preterm delivery. Using advanced machine learning algorithms, in conjunction with Electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. However, in this paper, the Electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilised, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. Seven different artificial neural networks were then used to identify these records. The results illustrate that the combination of the Levenberg–Marquardt trained Feed-Forward Neural Network, Radial Basis Function Neural Network and the Random Neural Network classifiers performed the best, with 91% for sensitivity, 84% for specificity, 94% for the area under the curve and 12% for the mean error rate.

Classification of pregnancy and labor contractions using a graph theory based analysis.

In this paper, we propose a new framework to characterize the electrohysterographic (EHG) signals recorded during pregnancy and labor. The approach is based on the analysis of the propagation of the uterine electrical activity. The processing pipeline includes i) the estimation of the statistical dependencies between the different recorded EHG signals, ii) the characterization of the obtained connectivity matrices using network measures and iii) the use of these measures in clinical application: the classification between pregnancy and labor. Due to its robustness to volume conductor, we used the imaginary part of coherence in order to produce the connectivity matrix which is then transformed into a graph. We evaluate the performance of several graph measures. We also compare the results with the parameter mostly used in the literature: the peak frequency combined with the propagation velocity (PV +PF). Our results show that the use of the network measures is a promising tool to classify labor and pregnancy contractions with a small superiority of the graph strength over PV+PF.

Selection algorithm for parameters to characterize uterine EHG signals for the detection of preterm labor

This article proposes a selection method that can be applied to choose the best parameters to classify contractions in the uterine electrohysterography (EHG) signal for the detection of preterm labor. Several types of parameters have historically been extracted from the electrohysterogram. These can be divided into three classes: linear parameters, nonlinear parameters and parameters related to the electrohyterogram propagation. Frequency band enhancement EHG characterization has also been extensively studied. Our work is divided in two parts. The first part is to implement and compute all the parameters already extracted from the EHG that have been published in the literature. These parameters were computed both on the original EHG and on different frequency bands obtained using wavelet packet decomposition. In the second part, we will use a new parameters selection method to eliminate all parameters that are not efficient and pertinent for classification. Our results indicate a set of 13 linear parameters, 3 nonlinear parameters and 2 propagation parameters that are potentially most useful to discriminate between pregnancy and labor contractions, either on different frequency bands or directly on original EHG.

Velocity and directionality of the electrohysterographic signal propagation.

ObjectiveThe initiation of treatment for women with threatening preterm labor requires effective distinction between true and false labor. The electrohysterogram (EHG) has shown great promise in estimating and classifying uterine activity. However, key issues remain unresolved and no clinically usable method has yet been presented using EHG. Recent studies have focused on the propagation velocity of the EHG signals as a potential discriminator between true and false labor. These studies have estimated the propagation velocity of individual spikes of the EHG signals. We therefore focus on estimating the propagation velocity of the entire EHG burst recorded during a contraction in two dimensions.Study DesignEHG measurements were performed on six women in active labor at term, and a total of 35 contractions were used for the estimation of propagation velocity. The measurements were performed using a 16-channel two-dimensional electrode grid. The estimates were calculated with a maximum-likelihood approach.ResultsThe estimated average propagation velocity was 2.18 (±0.68) cm/s. No single preferred direction of propagation was found.ConclusionThe propagation velocities estimated in this study are similar to those reported in other studies but with a smaller intra- and inter-patient variation. Thus a potential tool has been established for further studies on true and false labor contractions.

Automatic Identification of Motion Artifacts in EHG Recording for Robust Analysis of Uterine Contractions

Electrohysterography (EHG) is a noninvasive technique for monitoring uterine electrical activity. However, the presence of artifacts in the EHG signal may give rise to erroneous interpretations and make it difficult to extract useful information from these recordings. The aim of this work was to develop an automatic system of segmenting EHG recordings that distinguishes between uterine contractions and artifacts. Firstly, the segmentation is performed using an algorithm that generates the TOCO-like signal derived from the EHG and detects windows with significant changes in amplitude. After that, these segments are classified in two groups: artifacted and nonartifacted signals. To develop a classifier, a total of eleven spectral, temporal, and nonlinear features were calculated from EHG signal windows from 12 women in the first stage of labor that had previously been classified by experts. The combination of characteristics that led to the highest degree of accuracy in detecting artifacts was then determined. The results showed that it is possible to obtain automatic detection of motion artifacts in segmented EHG recordings with a precision of 92.2% using only seven features. The proposed algorithm and classifier together compose a useful tool for analyzing EHG signals and would help to promote clinical applications of this technique.

Electrohysterography of labor contractions: propagation velocity and direction

Electrohysterographic assessment of the propagation velocity of uterine depolarization has been introduced as a promising predictor of preterm labor. Therefore, the objectives of this study were to characterize the uterine electrohysterographic signals during labor and to determine the propagation velocity and propagation direction of electrohysterographic signals. Descriptive study. Department of Obstetrics and Gynecology, Aarhus University Hospital, Denmark. Eight women in active labor at term. Electrohysterograms (three channels) were recorded using surface electrodes placed abdominally along the vertical median axis with an inter-electrode distance of 6.5-11.2 cm. In total, 89 contractions were analyzed. Electrohysterographic characteristics: The duration of the contractions was 61.0 ± 18.0 s (mean ± SD). The median frequency of the power spectrum density was 0.51 (0.44; 0.51) Hz (median; 10th; 90th percentile). The greatest signal magnitude was obtained by the electrode in the centermost position. The propagation velocity: 2.15 (0.66; 13.8) cm/s in the upper part and 1.53 (0.58; 6.7) cm/s in the lower part of the uterus. Propagation direction: Both downward (58%) and upward (42%) propagation of the electrohysterographic signals occurred. Moreover, downward and upward propagations were recorded simultaneously in the upper and lower part of the uterus, suggesting a multidirectional propagation pattern. Labor contractions, expressed by electrohysterographic signals, propagate both in the downward and upward direction, a phenomenon that must be taken into account when determining the propagation velocity for preterm labor diagnostics.

A Novel Scheme to Classify EHG Signal for Term and Pre-term Pregnancy Analysis

prediction of premature pregnancy reduces neonatal death and helps in adoption of treatment well suited for the pre-term pregnancy state. There are scads of work done in the area of term and pre-term pregnancy analysis like artificial intelligence, regressive models, and higher order statistical models. This paper proposes a four-level decomposition of Electrohysterography (EHG) signals using Discrete Wavelet Transform (DWT) based on pyramid algorithm to obtain the final feature vector matrix. Classification is done using Support Vector Machines (SVM) by dividing the data into test and training sets. It is validated on a well known benchmark database from Physionet Database. The proposed method can be used for real time implementation owing to low computational cost, high speed and its feasibility to be implemented on hardware. The encouraging experimental results show that the technique gives an accuracy of 97.8% and can be a promising tool for investigating the risk of preterm labor.

Modeling and Identification of the Electrohysterographic Volume Conductor by High-Density Electrodes

The surface electrohysterographic (EHG) signal represents the bioelectrical activity that triggers the mechanical contraction of the uterine muscle. Previous work demonstrated the relevance of the EHG signal analysis for fetal and maternal monitoring as well as for prognosis of preterm labor. However, for the introduction in the clinical practice of diagnostic and prognostic EHG techniques, further insights are needed on the properties of the uterine electrical activation and its propagation through biological tissues. An important contribution for studying these phenomena in humans can be provided by mathematical modeling. A five-parameter analytical model of the EHG volume conductor and the cellular action potential (AP) is proposed here and tested on EHG signals recorded by a grid of 64 high-density electrodes. The model parameters are identified by a least-squares optimization method that uses a subset of electrodes. The parameters representing fat and abdominal muscle thickness are also measured by echography. The mean correlation coefficient and standard deviation of the difference between the echographic and EHG estimates were 0.94 and 1.9 mm, respectively. No bias was present. These results suggest that the model provides an accurate description of the EHG AP and the volume conductor, with promising perspectives for future applications.

Inter-electrode delay estimators for electrohysterographic propagation analysis

Premature birth is a major cause of mortality and permanent dysfunctions. Several parameters derived from single channel electrohysterographic (EHG) signals have been considered to determine contractions leading to preterm delivery. The results are promising, but improvements are needed. As effective uterine contractions result from a proper action potential propagation, in this paper we focus on the propagation properties of EHG signals, which can be predictive of preterm delivery. Two standard delay estimators, namely maximization of the cross-correlation function and spectral matching, are adapted and implemented for the assessment of inter-electrode delays of propagating EHG signals. The accuracy of the considered standard estimators might be hampered by a poor inter-channel correlation. An improved dedicated approach is therefore proposed. By simultaneous adaptive estimation of the volume conductor transfer function and the delay, a dedicated method is conceived for improving the inter-channel signal similarity during delay calculation. Furthermore, it provides delay estimates without resolution limits and it is suitable for low sampling rates, which are appropriate for EHG recording. The three estimators were evaluated on EHG signals recorded on seven women. The dedicated approach provided more accurate estimates due to a 22% improvement of the initial average inter-channel correlation.

Estimation of internal uterine pressure by joint amplitude and frequency analysis of electrohysterographic signals

Monitoring the uterine contraction provides important prognostic information during pregnancy and parturition. The existing methods employed in clinical practice impose a compromise between reliability and invasiveness. A promising technique for uterine contraction monitoring is electrohysterography (EHG). The EHG signal measures the electrical activity which triggers the contraction of the uterine muscle. In this paper, a non-invasive method for intrauterine pressure (IUP) estimation by EHG signal analysis is proposed. The EHG signal is regarded as a non-stationary signal whose frequency and amplitude characteristics are related to the IUP. After acquisition in a multi-channel configuration, the EHG signal is therefore analyzed in the time–frequency domain. A first estimation of the IUP is then derived by calculation of the unnormalized first statistical moment of the frequency spectrum. The estimation accuracy is finally increased by identification of a second-order polynomial model. The proposed method is compared to root mean squared analysis and optimal linear filtering and validated by simultaneous measurement of the IUP on nine women during labor. The results suggest that the proposed EHG signal analysis provides an accurate estimate of the IUP.

Quantitative analysis of contraction patterns in electrical activity signal of pregnant uterus as an alternative to mechanical approach

Monitoring of uterine contraction activity is an important diagnostic tool used during both pregnancy and labour. The strain the pregnant uterus exerts on the maternal abdomen is measured via external tocography. However, limitation of this approach has caused the development of another technique—electrohysterography—which is based on the recording of electrical uterine activity. A computer-aided system is presented, which allows the recording of electrohysterographic signals from the maternal abdomen and their on-line analysis both in time and frequency domains. As a research material, we acquired 108 traces during a 24 h period before labour from a group of patients between 37 and 40 weeks of gestation. The comparison study between electrohysterography and tocography was carried out thanks to the possibility of simultaneous recording of mechanical and electrical uterine activities. The obtained results show that both methods demonstrate high agreement in relation to the number of contractions recognized as being consistent. However, their agreement in relation to the quantitative description of recognized patterns has appeared to be unacceptable to consider these methods as fully alternative. The appropriate way of further development of electrohysterography seems to be spectral analysis. Several spectral parameters describing electrophysiological properties of uterine muscle can be obtained by the use of electrohysterographic signals.