Analysis of Various Signals Acquired from Uterine Contraction to determine true and false labor (original) (raw)
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EHG Signal Classification for True and False Pregnancy Analysis
– Normally pregnancy last for 40 week, in which babies are born normal and healthy. The babies which are born after 20th week and before 37th week of pregnancy are known as premature babies. This can cause deficiency in babies and high risk of death of child, to reduce neonatal death it is necessary to predict whether the labor is term or preterm one of the promising tool to measure the electrical activity of uterine muscle is EHG. Previous paper had done research in acquiring EHG signals, in this paper various EHG signal is acquired and linear and non-linear feature is extracted and is given to support vector machine (SVM) classifier to determine whether the signal is term or preterm.
A Review of Significant Researches on 4×4 EHG Signal for Classifying Uterine Contraction
INTERNATIONAL JOURNAL OF RECENT TRENDS IN ENGINEERING & RESEARCH, 2019
Pregnancy monitoring is the most important challenge during gestation period. Some early deliveries cause mortality and morbidity of the new born babies. Electrohysterogram is the most promising method for monitoring the uterine contraction thus the physiological wellbeing of the foetal and the mother. As it is a non-invasive method there are no side effects and its accuracy for early diagnosis is more. There are different numbers of electrodes used to collect the signals from the mother's abdomen by placing electrodes on the abdomen. Through this paper we are focusing on different researches used for pregnancy monitoring using 16 electrode database. Based on the studies this paper provides different steps in electrohysterogram data processing such as preprocessing, feature extraction, classifiers for classifying pregnancy and labour contraction.
Future Generation Computer Systems, 2018
Early diagnose for the prevention of preterm birth is one of the important perinatal challenges. The neonatal care and early treatment for preterm babies are increasing the chance of survival, but anyways it affects the respiratory distress, immature brains, cerebral palsy, mental retardation, visual and hearing impairments, and poor health and growth. If preterm labor is diagnosed in the early period of gestation, then it is easy to give an appropriate treatment to the pregnant woman. The uterine electrical activity assessment is a suitable method for monitoring the labor process especially for the prediction of preterm labor. Electrohysterography is a non-invasive technique to monitor the contraction. The electrohysterogram (EHG) or uterine electromyogram (Uterine EMG) is considered as a biomarker for the prediction or preterm labor. A number of studies in this field by various researchers have been reviewed. On the basis of such reviews, this paper provides the different steps such as preprocessing , feature extraction, classifiers and feature subset selection methods for the detection and prediction of preterm birth.
Classification of uterine EMG signals using supervised classification method
Journal of Biomedical Science and Engineering, 2010
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.
Signal, Image and Video Processing, 2014
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.
Nonlinearity of EHG signals used to distinguish active labor from normal pregnancy contractions
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2010
Labor prediction using the electrohysterogram has immediate clinical applications and has been the aim of several studies in recent years. Studies using various linear methods such as classic spectral analysis do not give clinically useful results. In this paper we present the use of two methods that investigate nonlinearity to predict normal labor. We show the comparison between a linear method that is known from the literature (mean power frequency) and two nonlinear methods (approximate entropy and time reversibility) using ROC analysis. The comparison indicates that the best method for pretreatment to classify pregnancy and labor signals is time reversibility. The results indicate that time reversibility is a very promising tool for distinguishing between labor and physiological contractions during pregnancy. This could be the first step in developing a clinical application method to predict preterm labor.
Identification of True and False Labor Through Uterine Contraction Signals
2021
Woman health care during pregnancy period is utmost important job and with-it safe delivery of child by identifying the true labor is another skill and challenging task for experts. Early assessment of first stage of true labor is significant task so that expert can take care and can provide the necessary aid to the laboring mother to avoid the risk of mothers and child life. Maternal mortality is unacceptably high in under developed countries like India, which contributes one-fifth of total maternal deaths globally. The motivational factors and the necessity to develop a system for differentiating True and False Labor based on Uterine Contraction are discussed in this paper. The objective of the research is to develop the diagnostic system for the analysis of uterine contraction for differentiation of true and false labor with advantage of early detection of true labor. The system is experimented under the supervision of expert clinician and the results of these experiments are dis...
Prediction of labor onset type: Spontaneous vs induced; role of electrohysterography?
Computer Methods and Programs in Biomedicine, 2017
BACKGROUND AND OBJECTIVE Induction of labor (IOL) is a medical procedure used to initiate uterine contractions to achieve delivery. IOL entails medical risks and has a significant impact on both the mother's and newborn's well-being. The assistance provided by an automatic system to help distinguish patients that will achieve labor spontaneously from those that will need late-term IOL would help clinicians and mothers to take an informed decision about prolonging pregnancy. With this aim, we developed and evaluated predictive models using not only traditional obstetrical data but also electrophysiological parameters derived from the electrohysterogram (EHG). METHODS EHG recordings were made on singleton term pregnancies. A set of 10 temporal and spectral parameters was calculated to characterize EHG bursts and a further set of 6 common obstetrical parameters was also considered in the predictive models design. Different models were implemented based on single layer Support Vector Machines (SVM) and with aggregation of majority voting of SVM (double layer), to distinguish between the two groups: term spontaneous labor (≤41 weeks of gestation) and IOL late-term labor. The areas under the curve (AUC) of the models were compared. RESULTS The obstetrical and EHG parameters of the two groups did not show statistically significant differences. The best results of non-contextualized single input parameter SVM models were achieved by the Bishop Score (AUC=0.65) and GA at recording time (AUC=0.68) obstetrical parameters. The EHG parameter median frequency, when contextualized with the two obstetrical parameters improved these results, reaching AUC=0.76. Multiple input SVM obtained AUC=0.70 for all EHG parameters. Aggregation of majority voting of SVM models using contextualized EHG parameters achieved the best result AUC=0.93. CONCLUSIONS Measuring the electrophysiological uterine condition by means of electrohysterographic recordings yielded a promising clinical decision support system for distinguishing patients that will spontaneously achieve active labor before the end of full term from those who will require late term IOL. The importance of considering these EHG measurements in the patient's individual context was also shown by combining EHG parameters with obstetrical parameters. Clinicians considering elective labor induction would benefit from this technique.
Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor
Computational and Mathematical Methods in Medicine, 2013
Numerous types of linear and nonlinear features have been extracted from the electrohysterogram (EHG) in order to classify labor and pregnancy contractions. As a result, the number of available features is now very large. The goal of this study is to reduce the number of features by selecting only the relevant ones which are useful for solving the classification problem. This paper presents three methods for feature subset selection that can be applied to choose the best subsets for classifying labor and pregnancy contractions: an algorithm using the Jeffrey divergence (JD) distance, a sequential forward selection (SFS) algorithm, and a binary particle swarm optimization (BPSO) algorithm. The two last methods are based on a classifier and were tested with three types of classifiers. These methods have allowed us to identify common features which are relevant for contraction classification.
Analysis of Unipolar and Bipolar 4x4 EHG Signal for Classifying Uterine Contraction
Biomedical and Pharmacology Journal, 2019
Proper evaluation and detection of uterine contraction is an important treat during gestation period. Uterine contraction happens by the generation of electrical activity from a given myometrial cell to the adjacent cell. There are various methods for monitoring uterine contraction but they lack to distinguish true labour contractions (efficient) from contractions that will not cause delivery (inefficient). One of the most accurate non-invasive technique for monitoring uterine contraction is the uterine electromyogram or Electrohysterogram (EHG). The main aim of this paper is to check whether it is possible to discriminate labour and pregnancy contraction by using 16 electrode database. And also to check bipolar signals give better classification rate than monopolar signals. Result shows that bipolar signal have better performance than monopolar signals.