Uterine Electromyography Signals Research Articles

Mid-trimester uterine electromyography in patients with a short cervix

Preterm birth is the largest single cause of infant death in the United States. A cervical length of <2.5 cm, measured in the mid-trimester, has been shown to identify individuals at increased risk. Uterine electromyography is an emerging technology for noninvasively assessing uterine bioelectrical activity. With its ability to characterize nuanced differences in myometrial signals, uterine electromyography assessments during the mid-trimester may provide insight into the mechanisms of cervical shortening. This study aimed to characterize uterine bioelectrical activity in pregnant individuals with short cervices in the mid-trimester compared with that of pregnant individuals of the same gestational age with normal cervical lengths. This is a prospective cohort study of subjects with singleton, nonanomalous pregnancies between 16 weeks and 0 days and 22 weeks and 6 days of gestational age. Subjects with normal cervical length (≥3.0 cm) were compared with subjects with short cervical length (<2.5 cm). The short-cervical-length cohort was further stratified by history of preterm birth. Multichannel uterine electromyography recordings were obtained for ∼60 minutes using proprietary, directional electromyography sensors on the abdomen. Uterine electromyography signals were observed and classified in groups as spikes, short bursts, and bursts. Primary outcomes were relative expression of spike, short-burst, and burst uterine electromyography signals. Subgroup analyses assessed each signal percentage by cervical length, history of preterm birth, and gestational age at delivery. Differences in percentage of uterine electromyography signals according to cervical length were analyzed using nonparametric tests of significance. Of the 28 included subjects, 10 had normal and 18 had short cervical length. There were 9 subjects with short cervical length and a history of preterm birth. Spikes were the most commonly recorded signals and were higher in the normal-cervical-length cohort (96.3% [interquartile range, 93.1%-100.0%]) than the short-cervical-length cohort (75.2% [interquartile range, 66.7%-92.0%], P=.001). In contrast, median percentages of short-bursts and bursts were significantly higher in subjects with a short cervical length (17.3% [interquartile range, 13.6%-23.9%] vs 2.5% for normal cervical length [interquartile range, 0%-5.5%], P=.001 and 6.6% [interquartile range, 0%-13.4%] vs 0% for normal cervical length [interquartile range, 0%-2.8%], P=.014, respectively). Within subgroup analyses, cervical length was inversely proportional to percentage of observed short-bursts (P=.013) and bursts (P=.014). Subjects with short cervical length and history of preterm birth had higher burst percentages (12.8% [interquartile range, 9.0%-15.7%]) than those with short cervical length and no history of preterm birth (3.3% [interquartile range, 0%-5.0%], P=.003). Short-burst and burst uterine electromyography signals are observed more frequently in mid-trimester patients with short cervical lengths. This relationship provides insight into abnormal myometrial activation in the mid-trimester and offers a plausible biophysiological link to cervical shortening.

Analysis of uterine electromyography signals using time-frequency based topological features for detection of preterm birth

Abstract Detection of preterm birth (gestational week &lt; 37) is a global priority as it causes major health problems to neonates. Assessment of uterine contractions (burst) is required to detect and prevent the threat of preterm birth. Uterine electromyography (uEMG) is widely preferred to measure the uterine contractions noninvasively. These signals are nonstationary in nature. It can be handled by topological data analysis (TDA) effectively. Therefore, TDA can be used to explore the characteristics of uEMG burst signals. In this study, an attempt has been made to distinguish term (gestational week ≥ 37) and preterm conditions using timefrequency based topological features in uEMG burst signals. These signals are obtained from the publicly available online dataset. The annotated burst signals are segmented and subjected to a short time Fourier transform. The transformed real and imaginary Fourier coefficients are plotted in the complex plane and the envelope of the data points are computed using the alpha-shape technique. Four topological features such as, area, perimeter, circularity and ellipse variance are extracted. These features are statistically analyzed. The coefficient of variation (CoV) is calculated to measure the inter-subject variations. The results show that the proposed method is able to discriminate between term and preterm conditions. The extracted features namely, area and perimeter exhibit significant difference (p &lt; 0.05) between these two conditions. The CoV of the perimeter is observed to be low, implying that this feature can handle inter-subject variations in burst signals. The extracted topological features are useful to analyze the characteristics of term and preterm pregnancies

Analysis of Frequency Bands of Uterine Electromyography Signals for the Detection of Preterm Birth.

In this work, an attempt has been made to analyze the influence of the frequencies bands in uterine electromyography (uEMG) signals on the detection of preterm birth. The signals recorded from the women's abdomen during pregnancy are considered in this study. The signals are subjected to preprocessing using digital bandpass Butterworth filter and decomposed into different frequency bands namely, 0.3-1.0 Hz (F1), 1.0-2.0 Hz (F2) and 2.0-3.0Hz (F3). Spectral features namely, peak magnitude, peak frequency, mean frequency and median frequency are extracted from the power spectrum. Classification models namely, k-nearest neighbor, support vector machine and random forest are employed to distinguish the term and preterm conditions. The results show that the features extracted from these frequency bands are able to differentiate term and preterm condition. Particularly, the frequency band F3 performs better than other frequency bands. The features associated with these frequencies along with random forest classification model achieves a maximum accuracy of 75.2%. Thus, these measures could be used to accurately detect the preterm birth well in advance.

Empirical Mode Decomposition based Detrended Fluctuation Analysis of Uterine Electromyography Signals in Term Conditions

In this study, an attempt has been made to analyze uterine Electromyography (uEMG) signals during Term conditions (pregnancy duration more than 37 weeks of gestational age) using Empirical Mode Decomposition based Detrended Fluctuation Analysis (EMD-DFA). The Term delivery signals are considered from a publicly available database, grouped as T1 (26 weeks gestational age) and T2 (≥26 weeks gestational age) based on the time of recording, and are subjected to an EMD-DFA algorithm. The double logarithmic plot of the detrended fluctuation function against the scale is analyzed, and the Hurst exponent feature is computed for both groups. The features are statistically analyzed using the Student’s t-test. Results show that the EMD-DFA method is able to capture the variations in the fluctuations of uEMG signals under these conditions. The Hurst exponent feature is found to be statistically significant (p-value 0.005) for both groups. There is an increase in the Hurst exponent feature value in the T2 group, which indicates that T2 signals possess smoother characteristics than T1 signals. Hence, it appears that the proposed approach could aid in investigating variations in the fluctuations of Term delivery signals, differentiating T1 and T2 conditions.

Classification of Nonlinear Features of Uterine Electromyogram Signal Towards the Prediction of Preterm Birth

Early detection of preterm labor is important to avoid neonatal death and mortality. Uterine electromyography (UEMG) or electrohysterography is a non-invasive method of extracting electrical activity signal from the abdominal part during pregnancy, which helps in early detection. This signal can be used to classify term and preterm labors. Herein, the performances of four classifiers have been evaluated using seven nonlinear features extracted from UEMG signals. They were then compared with four features analyzed from different literature. The results show that with the Elman neural network classifier, the bi-spectrum feature, which has phase information, outperforms other features with 99.8875% accuracy, 100% sensitivity, and 99.77% specificity.

Measurement of Uterine and Abdominal Muscle Electromyography in Pregnant Women for Estimation of Expulsive Activities during the 2nd Stage of Labor

Background: The progression of labor and delivery of the fetus is dependent upon uterine contractions and the voluntary effort of abdominal muscle contractions. A good monitor of uterine contractions and pushing is necessary for obstetrical care. Electromyography (EMG) is the underlying basis for contractility of muscle including the myometrium. Objectives: The aim of this study was to determine the relationship between EMG activity of uterine and abdominal muscles and the duration of the 2nd stage of labor in pregnant women. Methods: EMG of both uterine and abdominal muscles was simultaneously recorded from electrodes placed on the abdominal surface of 45 active 2nd stage-laboring nulliparous patients. EMG was recorded using filters to separate uterine and abdominal EMG signals, and various EMG signal parameters were analyzed. The duration of the 2nd stage of labor and other maternal and fetal characteristics were also recorded. Results: Uterine EMG bursts precede abdominal bursts and are accompanied by feelings of “urge to push” by the patients. Abdominal root mean square (RMS) and power, but not uterine EMG parameters, are reduced (p< 0.005) in patients with longer labors and linear regression analysis demonstrated a negative correlation to the duration of 2nd stage of labor (p < 0.001). Multivariate linear regression analysis of clinical characteristics (fetal weight, body mass index, placental location, etc.) and parameters of EMG showed that only abdominal RMS is negatively correlated with the duration of labor. Conclusions: (1) Uterine and abdominal EMG activities reflect the expulsive involuntary (uterine) and voluntary (abdominal) muscular activities during the 2nd stage of labor. (2) RMS and power of abdominal EMG diminish with longer labor when uterine EMG intensities are similar. (3) Recording of uterine and abdominal muscle activity provides objective evaluation of the muscle activity during the 2nd stage of labor and may aid in the evaluation of any interventions.

Multifractal analysis of uterine electromyography signals to differentiate term and preterm conditions.

In this study, an attempt has been made to identify the origin of multifractality in uterine electromyography signals and to differentiate term (gestational age > 37 weeks) and preterm (gestational age ≤ 37 weeks) conditions by multifractal detrended moving average technique. The signals obtained from a publicly available database, recorded from the abdominal surface during the second trimester, are used in this study. The signals are preprocessed and converted to shuffle and surrogate series to examine the source of multifractality. Multifractal detrended moving average algorithm is applied on all the signals. The presence of multifractality is verified using scaling exponents, and multifractal spectral features are extracted from the spectrum. The variation of multifractal features in term and preterm conditions is analyzed statistically using Student's t-test. The results of scaling exponents show that the uterine electromyography or electrohysterography signals reveal multifractal characteristics in term and preterm conditions. Further investigation indicates the existence of long-range correlation as the primary source of multifractality. Among all extracted features, strength of multifractality, exponent index, and maximum and peak singularity exponents are statistically significant ( p < 0.05) in differentiating term and preterm conditions. The coefficient of variation is found to be lower for strength of multifractality and peak singularity exponent, which reveal that these features exhibit less inter-subject variance. Hence, it appears that multifractal analysis can aid in the diagnosis of preterm or term delivery of pregnant women.

Multifractal Analysis of Uterine Electromyography Signals for the Assessment of Progression of Pregnancy in Term Conditions.

The objectives of this paper are to examine the source of multifractality in uterine electromyography (EMG) signals and to study the progression of pregnancy in the term (gestation period > 37 weeks) conditions using multifractal detrending moving average (MFDMA) algorithm. The signals for the study, considered from an online database, are obtained from the surface of abdomen during the second (T1) and third trimester (T2). The existence of multifractality is tested using Hurst and scaling exponents. With the intention of identifying the origin of multifractality, the preprocessed signals are converted to shuffle and surrogate data. The original and the transformed signals are subjected to MFDMA to extract multifractal spectrum features, namely strength of multifractality, maximum, minimum, and peak singularity exponents. The Hurst and scaling exponents extracted from the signals indicate that uterine EMG signals are multifractal in nature. Further analysis shows that the source of multifractality is mainly owing to the presence of long-range correlation, which is computed as 79.98% in T1 and 82.43% in T2 groups. Among the extracted features, the peak singularity exponent and strength of multifractality show statistical significance in identifying the progression of pregnancy. The corresponding coefficients of variation are found to be low, which show that these features have low intersubject variability. It appears that the multifractal analysis can help in investigating the progressive changes in uterine muscle contractions during pregnancy.

Analysis of uterine electromyography signals in preterm condition using multifractal algorithm.

In this work, an attempt has been made to analyze the preterm (gestation period $\leq 37$ weeks) condition using uterine electromyography (EMG) signals and multifractal detrended fluctuation analysis (MFDFA). The signals recorded from the electrodes placed on the surface of abdomen are used for this study and these are obtained from a publically available online database. These signals are preprocessed using 4-pole digital Butterworth filter. The preprocessed signals are subjected to MFDFA to extract multifractal features namely maximum singularity exponent, peak singularity exponent, strength of multifractality and exponent index. Generalized Hurst exponent extracted from the signals indicate that uterine EMG signals show multifractal behavior in preterm condition. Among the extracted features the coefficient of variation is found to be lower for peak singularity exponent. This indicates that this feature have lower inter-subject variability. Hence, it appears that the multifractal features can help in the assessment of uterine EMG signals for preterm detection.

Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals.

Preterm delivery increases the risk of infant mortality and morbidity, and therefore developing reliable methods for predicting its likelihood are of great importance. Previous work using uterine electromyography (EMG) recordings has shown that they may provide a promising and objective way for predicting risk of preterm delivery. However, to date attempts at utilizing computational approaches to achieve sufficient predictive confidence, in terms of area under the curve (AUC) values, have not achieved the high discrimination accuracy that a clinical application requires. In our study, we propose a new analytical approach for assessing the risk of preterm delivery using EMG recordings which firstly employs Empirical Mode Decomposition (EMD) to obtain their Intrinsic Mode Functions (IMF). Next, the entropy values of both instantaneous amplitude and instantaneous frequency of the first ten IMF components are computed in order to derive ratios of these two distinct components as features. Discrimination accuracy of this approach compared to those proposed previously was then calculated using six differently representative classifiers. Finally, three different electrode positions were analyzed for their prediction accuracy of preterm delivery in order to establish which uterine EMG recording location was optimal signal data. Overall, our results show a clear improvement in prediction accuracy of preterm delivery risk compared with previous approaches, achieving an impressive maximum AUC value of 0.986 when using signals from an electrode positioned below the navel. In sum, this provides a promising new method for analyzing uterine EMG signals to permit accurate clinical assessment of preterm delivery risk.

A machine learning system for automatic detection of preterm activity using artificial neural networks and uterine electromyography data

Preterm births are babies born before 37 weeks of gestation. The premature delivery of babies is a major global health issue with those affected at greater risk of developing short and long-term complications. Therefore, a better understanding of why preterm births occur is needed. Electromyography is used to capture electrical activity in the uterus to help treat and understand the condition, which is time consuming and expensive. This has led to a recent interest in automated detection of the electromyography correlates of preterm activity. This paper explores this idea further using artificial neural networks to classify term and preterm records, using an open dataset containing 300 records of uterine electromyography signals. Our approach shows an improvement on existing studies with 94.56% for sensitivity, 87.83% for specificity, and 94% for the area under the curve with 9% global error when using the multilayer perceptron neural network trained using the Levenberg-Marquardt algorithm.

New technique based on uterine electromyography nonlinearity for preterm delivery detection

Detecting uterine electromyography (EMG) signals can yield a promising approach to determine and take actions to prevent preterm deliveries. This paper objective is to predict this risk using such uterine signals. Previous classification studies have used only linear signal processing which depends on the spectral characteristics of the uterine EMG signals that did not give clinically acceptable results. On the other hand some studies have made linear and non-linear analysis for the signals and have found that the non-linear parameters can distinguish the preterm delivery in better way than the linear parameters. In this research, two methods will be taken; the first method is to take some linear parameters to a suitable neural network and the second one is to take some non-linear parameters to the same network. Then, the two results are compared by calculating parameters False Positive Rate, False Negative Rate, True Positive Rate, True Negative Rate and Accuracy to evaluate the classification performance. Besides, a linear parameter, discrete cosine transform, which depends on the spectral characteristics of the signals, is taken as an additional feature to the same network so the research will have a third method to illustrate the difference between the traditional previous classification method and the proposed ones. Applying the second method gives better results than the first and the third methods. The paper can propose a method depends on the uterine EMG nonlinearity which gives best results to detect preterm delivery compared with those used in previous studies. Key words: Uterine electromyography (EMG) signals, term-preterm deliveries prediction, neural network performance evaluation, discrete cosine transform.

Better Pregnancy Monitoring Using Nonlinear Correlation Analysis of External Uterine Electromyography

The objective of this paper is to evaluate the novel method for analyzing the nonlinear correlation of the uterine electromyography (EMG). The application of this method may improve monitoring in pregnancy, labor detection, and preterm labor detection. Uterine EMG signals recorded from a 4 × 4 matrix of electrodes on the subjects' abdomen are used here. The propagation was analyzed using the nonlinear correlation coefficient h(2). Signals from 49 women (36 during pregnancy and 13 in labor) at different gestational age were used. ROC curves were computed to evaluate the potential of three methods to differentiate between 174 contractions recorded during pregnancy and 115 contractions recorded during labor. The results indicate considerably better performance of the nonlinear correlation analysis (area under curve = 0.85) when compared to classical frequency parameters (area under curve = 0.76 and 0.66) in distinguishing labor contractions from normal pregnancy contractions. We conclude that the analysis of the propagation of the uterine electrical activity using the nonlinear correlation coefficient h(2) is a promising way of improving the usefulness of uterine EMG signals for clinical purposes, such as monitoring in pregnancy, labor detection, and prediction of preterm labor.

Classification of uterine EMG signals using supervised classification method

Aim: The main purpose of this article is to detect any risk of preterm deliveries at an early gestation period using uterine electromyography signals. Detecting such uterine signals can yield a promising approach to determine and take actions to prevent this potential risk. Methods: The best position for the detection of different uterine signals is the median vertical axis of the abdomen. These signals differ from each other by their frequency content. Initially, simulation is done for the real detected EMG signals: preterm deliveries (PD) EMGs and deliveries at term (DT) EMGs. This is performed by applying autoregressive model (AR) of specific order to estimate AR coefficients of these real EMG signals. Finally, after calculation of the AR parameters of the two types of deliveries, we generate two types of simulated uterine contractions by using White Gaussian Noise (WGN). Frequency parameter extraction and classification are first applied on simulated signals to test the limits and performance of the used methods. The last remaining step is the classification of the contractions using supervised classification method. Results: Results show that uterine contractions may be classified using the Artificial Neural Networks (ANNs). The Simple Perceptron ANN is applied on the signals for their supervised classification into independent groups: preterm deliveries (PD) and deliveries at term (TD) according to their frequency content.

Uterine electromyography signal feature extraction and classification

Uterine electromyography (EMG) signal has potential for early diagnosis of preterm labour clinically. But it is difficult to differentiate the EMG signal patterns leading to preterm birth. In this paper, the effort has been made to find effective algorithms for extracting the features from uterine EMG signals which can be used to classify the normal term labour from abnormal preterm labour signals. A combined algorithm has been proposed, in which the signal is firstly pre-processed to eliminate the noise and high frequency components. Then, the fractal dimension value along the signal is calculated to identify the abnormal values for distinguishing contraction patterns. Two techniques are employed: phase space reconstruction and singular value decomposition. Finally, the signals are classified using artificial neural network method. The experiment tests indicate that the method can be a choice for solving the problem described but more tests are required to draw final conclusion.

An unsupervised classification method of uterine electromyography signals: Classification for detection of preterm deliveries

This article proposes an unsupervised classification method that can be applied to the electromyography signal of uterine contractions for the detection of preterm birth. The frequency content of the electromyography changes from one woman to another, and during pregnancy, so wavelet decomposition is first used to extract the parameters of each contraction, and an unsupervised statistical classification method based on Fisher's test is used to classify the events. A principal component analysis projection is then used as evidence of the groups resulting from this classification. Another method of classification based on a competitive neural network is also applied on the same signals. Both methods are compared. Results show that uterine contractions may be classified into independent groups according to their frequency content and according to term (either at recording or at delivery).

Uterine electromyography signal feature extraction and classification

Uterine electromyography signal feature extraction and classification

Identification of term and preterm labor in rats using artificial neural networks on uterine electromyography signals

This study was undertaken to use artificial neural networks on uterine electromyography data to identify term and preterm labor in rats. Controls (group 1: n = 4) and preterm labor models (group 2: n = 4, treated with onapristone) were used. Uterine electromyography and intrauterine pressure (IUP) variables were measured by implanted telemetric devices. For each timepoint assessed, either a "labor event" or "nonlabor event" was first assigned by using visual and other means. 112 total labor and nonlabor events were observed. Artificial neural networks were then used with electromyography and intrauterine pressure parameters to attempt algorithmic, objective identification for time of labor in each group. For group 1, all 8 (100%) labor events and all 44 (100%) nonlabor events were correctly identified by the artificial neural networks. For group 2, 22 of 24 (92%) labor events and 31 of 36 (86%) nonlabor events were correctly determined by the artificial neural networks. Artificial neural networks can effectively predict term and preterm labor during pregnancy with the use of uterine electromyography and intrauterine pressure variables.