Uterine EHG processing for obstetrical monitoring

Cyclostationary analysis of uterine EMG measurements for the prediction of preterm birth

Preterm birth (gestational age < 37 weeks) is a public health concern that causes fetal and maternal mortality and morbidity. When this condition is detected early, suitable treatment can be prescribed to delay labour. Uterine electromyography (uEMG) has gained a lot of attention for detecting preterm births in advance. However, analyzing uEMG is challenging due to the complexities associated with inter and intra-subject variations. This work aims to investigate the applicability of cyclostationary characteristics in uEMG signals for predicting premature delivery. The signals under term and preterm situations are considered from two online datasets. Preprocessing is carried out using a Butterworth bandpass filter, and spectral correlation density function is adapted using fast Fourier transform-based accumulation method (FAM) to compute the cyclostationary variations. The cyclic frequency spectral density (CFSD) and degree of cyclostationarity (DCS) are quantified to assess the existence of cyclostationarity. Features namely, maximum cyclic frequency, bandwidth, mean cyclic frequency (MNCF), and median cyclic frequency (MDCF) are extracted from the cyclostationary spectrum and analyzed statistically. uEMG signals exhibit cyclostationarity property, and these variations are found to distinguish preterm from term conditions. All the four extracted features are noted to decrease from term to preterm conditions. The results indicate that the cyclostationary nature of the signals can provide better characterization of uterine muscle contractions and could be helpful in detecting preterm birth. The proposed method appears to aid in detecting preterm birth, as analysis of uterine contractions under preterm conditions is imperative for timely medical intervention.

An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery

The existing non-invasive automated preterm birth prediction methods rely on the use of uterine electrohysterogram (EHG) records coming from spontaneous preterm and term deliveries, and are indifferent to term induced and cesarean section deliveries. In order to enhance current publicly available pool of term EHG records, we developed a new EHG dataset, Induced Cesarean EHG DataSet (ICEHG DS), containing 126 30-minute EHG records, recorded early (23rd week), and/or later (31st week) during pregnancy, of those pregnancies that were expected to end in spontaneous term delivery, but ended in induced or cesarean section delivery. The records were collected at the University Medical Center Ljubljana, Ljubljana, Slovenia. The dataset includes 38 and 43, early and later, induced; 11 and 8, early and later, cesarean; and 13 and 13, early and later, induced and cesarean EHG records. This dataset enables better understanding of the underlying physiological mechanisms involved during pregnancies ending in induced and cesarean deliveries, and provides a robust and more realistic assessment of the performance of automated preterm birth prediction methods.

Characterization and separation of preterm and term spontaneous, induced, and cesarean EHG records

To improve the understanding of the underlying physiological processes that lead to preterm birth, and different term delivery modes, we quantitatively characterized and assessed the separability of the sets of early (23rd week) and later (31st week) recorded, preterm and term spontaneous, induced, cesarean, and induced-cesarean electrohysterogram (EHG) records using several of the most widely used non-linear features extracted from the EHG signals. Linearly modeled temporal trends of the means of the median frequencies (MFs), and of the means of the peak amplitudes (PAs) of the normalized power spectra of the EHG signals, along pregnancy (from early to later recorded records), derived from a variety of frequency bands, revealed that for the preterm group of records, in comparison to all other term delivery groups, the frequency spectrum of the frequency band B0L (0.08-0.3 Hz) shifts toward higher frequencies, and that the spectrum of the newly identified frequency band B0L' (0.125-0.575 Hz), which approximately matches the Fast Wave Low band, becomes stronger. The most promising features to separate between the later preterm group and all other later term delivery groups appear to be MF (p=1.1⋅10^-5) in the band B0L of the horizontal signal S3, and PA (p=2.4⋅10^-8) in the band B0L' (S3). Moreover, the PA in the band B0L' (S3) showed the highest power to individually separate between the later preterm group and any other later term delivery group. Furthermore, the results suggest that in preterm pregnancies the resting maternal heart rate decreases between the 23rd and 31st week of gestation.

Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram

The electrohysterogram (EHG) is the uterine muscle electromyogram recorded at the abdominal surface of pregnant or non-pregnant woman. The maternal respiration electromyographic signal (MR-EMG) is one of the most relevant interferences present in an EHG. Alvarez (Alv) waves are components of the EHG that have been indicated as having the potential for preterm and term birth prediction. The MR-EMG component in the EHG represents an issue, regarding Alv wave application for pregnancy monitoring, for instance, in preterm birth prediction, a subject of great research interest. Therefore, the Alv waves denoising method should be designed to include the interference MR-EMG attenuation, without compromising the original waves. Adaptive filter properties make them suitable for this task. However, selecting the optimal adaptive filter and its parameters is an important task for the success of the filtering operation. In this work, an algorithm is presented for the automatic adaptive filter and parameter selection using synthetic data. The filter selection pool comprised sixteen candidates, from which, the Wiener, recursive least squares (RLS), householder recursive least squares (HRLS), and QR-decomposition recursive least squares (QRD-RLS) were the best performers. The optimized parameters were L = 2 (filter length) for all of them and λ = 1 (forgetting factor) for the last three. The developed optimization algorithm may be of interest to other applications. The optimized filters were applied to real data. The result was the attenuation of the MR-EMG in Alv waves power. For the Wiener filter, power reductions for quartile 1, median, and quartile 3 were found to be -16.74%, -20.32%, and -15.78%, respectively (p-value = 1.31 × 10^-12).

Regional identification of information flow termination of electrohysterographic signals: Towards understanding human uterine electrical propagation

BACKGROUND AND OBJECTIVE

The uterine electrohysterogram (EHG) contains important information about electrical signal propagation which may be useful to monitor and predict the progress of pregnancy towards parturition. Directed information processing has the potential to be of use in studying EHG recordings. However, so far, there is no directed information-based estimation scheme that has been applied to investigating the propagation of human EHG recordings. To realize this, the approach of directed information and its reliability and adaptability should be scientifically studied.

METHODS

We demonstrated an estimation scheme of directed information to identify the spatiotemporal relationship between the recording channels of EHG signal and assess the algorithm reliability initially using simulated data. Further, a regional identification of information flow termination (RIIFT) approach was developed and applied for the first time to extant multichannel EHG signals to reveal the terminal zone of propagation of the electrical activity associated with uterine contraction. RIIFT operates by estimating the pairwise directed information between neighboring EHG channels and identifying the location where there is the strongest inward flow of information. The method was then applied to publicly-available experimental data obtained from pregnant women with the use of electrodes arranged in a 4-by-4 grid.

RESULTS

Our results are consistent with the suggestions from the previous studies with the added identification of preferential sites of excitation termination - within the estimated area, the direction of surface action potential propagation towards the medial axis of uterus during contraction was discovered for 72.15% of the total cases, demonstrating that our RIIFT method is a potential tool to investigate EHG propagation for advancing our understanding human uterine excitability.

CONCLUSIONS

We developed a new approach and applied it to multichannel human EHG recordings to investigate the electrical signal propagation involved in uterine contraction. This provides an important platform for future studies to fill knowledge gaps in the spatiotemporal patterns of electrical excitation of the human uterus.

Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).

Alvarez waves in pregnancy: a comprehensive review

Alvarez waves are local rhythmic contractions of the myometrium with high frequency and low intensity. They can be detected using internal or external tocography and electrohysterography. Some researchers correlate these small contractions with the initiation of labor, since they have been described as a pattern representing the uterine response to prostaglandin production. Other authors either do not validate a causality relation between Alvarez waves and labor or suggest that they have low predictive value for preterm labor. Alvarez waves' research has become a multidisciplinary subject with inputs ranging from medical science, biomedical engineering, and related areas. A comprehensive review is herein conducted to summarize the state of the art regarding Alvarez waves and their role in the initiation of labor, namely in preterm birth. The results show that a large number of studies have analyzed and characterized Alvarez waves without necessarily digging into their relationship with labor. Publications were categorized in three groups: (A) reports about morphology and characterization of Alvarez waves; (B) publications reporting a positive causality relation between Alvarez waves and labor; and (C) publications reporting an absence of causality regarding the previous hypothesis. Studies in group B outnumbered those in group C. A critical analysis is presented.

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part I: Cardiac Signals

Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today’s clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.

Electrodes in external electrohysterography: a systematic literature review

BACKGROUND

In low-income countries, pregnant women do not have easy access to health care, especially in rural and peri-urban areas. In this context, they can be surprised by the uterine contractions that precede childbirth and sometimes find themselves giving birth at home or on the way to the nearest health facility (located miles away from their home). In view of the development of an external uterine electrohysterogram acquisition system for labour prediction, a review of the literature on electrodes and their characteristics is necessary.

METHODS

A comprehensive literature review was conducted to collate information on the use of electrodes in external EHG recording and their characteristics.

RESULTS

Wet electrodes based on Ag/AgCl redox chemistry are the most common type of electrodes for EHG, employed in different configurations on the pregnant woman's abdomen. All positioning configurations are around the vertical median axis if they are not placed directly on it. Positioning below the navel seems to be the most efficient. The number of source, reference, and ground electrodes used varies from one author to another, as does the distance between the electrodes.

CONCLUSION

Two well-positioned source electrodes on the vertical median axis, with ground electrode on the right side of the hip and reference one on the left side, are able to generate a good external EHG recording signal. The minimum allowed inter-electrode distance is approximately 17.5 to 25mm.

Assessing Velocity and Directionality of Uterine Electrical Activity for Preterm Birth Prediction Using EHG Surface Records

The aim of the present study was to assess the capability of conduction velocity amplitudes and directions of propagation of electrohysterogram (EHG) waves to better distinguish between preterm and term EHG surface records. Using short-time cross-correlation between pairs of bipolar EHG signals (upper and lower, left and right), the conduction velocities and their directions were estimated using preterm and term EHG records of the publicly available Term–Preterm EHG DataSet with Tocogram (TPEHGT DS) and for different frequency bands below and above 1.0 Hz, where contractions and the influence of the maternal heart rate on the uterus, respectively, are expected. No significant or preferred continuous direction of propagation was found in any of the non-contraction (dummy) or contraction intervals; however, on average, a significantly lower percentage of velocity vectors was found in the vertical direction, and significantly higher in the horizontal direction, for preterm dummy intervals above 1.0 Hz. The newly defined features—the percentages of velocities in the vertical and horizontal directions, in combination with the sample entropy of the EHG signal recorded in the vertical direction, obtained from dummy intervals above 1.0 Hz—showed the highest classification accuracy of 86.8% (AUC=90.3%) in distinguishing between preterm and term EHG records of the TPEHGT DS.

Analysis of Electrohysterographic Signal Propagation Direction during Uterine Contraction: the Application of Directed Information

The potential of using the information of uterine contractions (UCs) derived from electrohysterogram (EHG) has been recognized in early detection of preterm delivery. A better understanding of the conduction property of EHG is clinically useful for developing advanced methods to achieve a reliable prediction of preterm delivery. In this paper, a method to analyze the destination of EHG propagation has been proposed via the estimation of directed information (DI) between each pair of neighboring channels with a novel propagation terminal zone (PTZ) identification algorithm. The proposed method was applied to experimental data from the Icelandic 16-electrode EHG database. The results demonstrated that for more than 81.8% participants, the PTZ was identified along the medial axis of uterus, among which more than half have their PTZ determined in the center between the uterine fundus and public symphysis, which indicated a great probability of propagation of EHG signals towards the center of uterus plane.Clinical relevance- This study makes a fundamental contribution for predicting preterm delivery, which can provide improvement in obstetric care towards pregnancy monitoring.

Uterine contractions clustering based on electrohysterography

The uterine electromyogram, also named Electrohysterogram (EHG), is a non-invasive technique that has been used for pregnancy and labour monitoring as well as for research work on uterine physiology. This technique is well established in this field. There is however a vast unexplored potential in the EHG that is currently the subject of interdisciplinary research work involving different scientific fields such as medicine, engineering, physics and mathematics. In this paper, an unsupervised clustering method is applied to a previously obtained set of frequency spectral representations of the respective EHG signal contractions that were previously automatically detected and delineated. An innovative approach using the complete spectrum projection is described, rather than a set of relevant points. The feasibility of the method is established despite the concerns of possible computational burden incurred by the processing of the whole spectrum. Given the unsupervised nature of this classification, a validation procedure was performed whereas the obtained clusters were labelled through the correlation with the common knowledge about the most relevant uterine contraction types, as described in the literature. As a result of this study, a spectral description of the Alvarez contractions was obtained where it was possible to breakdown these important events in two different types according to their spectrum. Spectral estimates of Braxton-Hicks contractions were also obtained and associated to one of the clusters. This led to a full spectral characterization of these uterine events.

Critical analysis of electrohysterographic methods for continuous monitoring of intrauterine pressure

Monitoring the progression of uterine activity provides important prognostic information during pregnancy and delivery. Currently, uterine activity monitoring relies on direct or indirect mechanical measurements of intrauterine pressure (IUP), which are unsuitable for continuous long-term observation. The electrohysterogram (EHG) provides a non-invasive alternative to the existing methods and is suitable for long-term ambulatory use. Several published state-of-the-art methods for EHG-based IUP estimation are here discussed, analyzed, optimized, and compared. By means of parameter space exploration, key parameters of the methods are evaluated for their relevance and optimal values. We have optimized all methods towards higher IUP estimation accuracy and lower computational complexity. Their accuracy was compared with the gold standard accuracy of internally measured IUP. Their computational complexity was compared based on the required number of multiplications per second (MPS). Significant reductions in computational complexity have been obtained for all published algorithms, while improving IUP estimation accuracy. A correlation coefficient of 0.72 can be obtained using fewer than 120 MPS. We conclude that long-term ambulatory monitoring of uterine activity is possible using EHG-based methods. Furthermore, the choice of a base method for IUP estimation is less important than the correct selection of electrode positions, filter parameters, and postprocessing methods. The presented review of state-of-the-art methods and applied optimizations show that long-term ambulatory IUP monitoring is feasible using EHG measurements.

An Automatic Method for the Segmentation and Classification of Imminent Labor Contraction From Electrohysterograms

OBJECTIVE

Preterm birth is the first cause of perinatal morbidity and mortality. Despite continuous clinical routine improvements, the preterm rate remains steady. Moreover, the specificity of the early diagnosis stays poor as many hospitalized women for preterm delivery threat finally deliver at term. In this context, the use of electrohysterograms may increase the sensitivity and the specificity of early diagnosis of preterm labor.

METHODS

This paper proposes a clinical application of electrohysterogram processing for the classification of patients as prone to deliver within a week or later. The approach relies on non-linear correlation analysis for the contraction bursts extraction and uses computation of various features combined with the use of Gaussian mixture models for their classification. The method is tested on a new dataset of 68 records collected on women hospitalized for preterm delivery threat.

RESULTS

This paper presents promising results for the automatic segmentation of the contraction and a classification sensitivity, specificity, and accuracy of, respectively, 80.7%, 76.3%, and 76.2%.

CONCLUSION

These results are in accordance with the gold standards but have the advantage to be non-invasive and could be performed at home.

SIGNIFICANCE

Diagnosis of imminent labor is possible by electrohysterography recording and may help in avoiding over-medication and in providing better cares to at-risk pregnant women.

Development of Electrohysterogram Recording System for Monitoring Uterine Contraction

Uterine contraction (UC) is an important clinical indictor for monitoring uterine activity. The purpose of this study is to develop a portable electrohysterogram (EHG) recording system (called PregCare) for monitoring UCs with EHG signals. The PregCare consisted of sensors, a signal acquisition device, and a computer with application software. Eight-channel EHG signals, the tocodynamometry (TOCO) signal, and maternal perception were recorded simultaneously by the signal acquisition device controlled by the computer via Bluetooth. PregCare was firstly evaluated by a signal simulator. Its relative error (RE) and coefficient of variation (CV) were calculated, and its agreement with the commercial instrument PowerLab was assessed by Bland-Altman plots. After that, PregCare was applied to 20 pregnant women in a hospital to record their EHG signals. These EHG signals were preprocessed and segmented into UCs and non-UCs. Then, the EHG features corresponding to UCs and non-UCs were extracted, respectively, including power spectral density (PSD), root mean square (RMS), peak frequency (PF), median frequency (MDF), and sample entropy (SamEn). One-way ANOVA was employed to assess the difference between UCs and non-UCs. The results show that RE and CV were less than 8% and 0.03%, respectively, which indicated the high accuracy and repeatability of PregCare. The small differences of mean and standard deviation indicated the high agreement between PregCare and PowerLab. Besides, the PSD of UCs was much larger than non-UCs between 0 and 0.7 Hz. RMS of UCs was significantly larger than non-UCs (p < 0.05). PF and SamEn of UCs were significantly smaller than non-UCs (p < 0.05). In conclusion, the developed EHG recording system was able to record EHG signals reliably. It has the advantages of portability, low power consumption, and wireless transmission, which can be used for long-term monitoring of UCs and prediction of the preterm delivery.

Application of decision tree in determining the importance of surface electrohysterography signal characteristics for recognizing uterine contractions

The aims of this study were to apply decision tree to classify uterine activities (contractions and non-contractions) using the waveform characteristics derived from different channels of electrohysterogram (EHG) signals and then rank the importance of these characteristics. Both the tocodynamometer (TOCO) and 8-channel EHG signals were simultaneously recorded from 34 healthy pregnant women within 24 h before delivery. After preprocessing of EHG signals, EHG segments corresponding to the uterine contractions and non-contractions were manually extracted from both original and normalized EHG signals according to the TOCO signals and the human marks. 24 waveform characteristics of the EHG segments were derived separately from each channel to train the decision tree and classify the uterine activities. The results showed the Power and sample entropy (SamEn) extracted from the un-normalized EHG segments played the most important roles in recognizing uterine activities. In addition, the EHG signal characteristics from channel 1 produced better classification results (AUC = 0.75, Sensitivity = 0.84, Specificity = 0.78, Accuracy = 0.81) than the others. In conclusion, decision tree could be used to classify the uterine activities, and the Power and SamEn of un-normalized EHG segments were the most important characteristics in uterine contraction classification.

Characterization and automatic classification of preterm and term uterine records

Predicting preterm birth is uncertain, and numerous scientists are searching for non-invasive methods to improve its predictability. Current researches are based on the analysis of ElectroHysteroGram (EHG) records, which contain information about the electrophysiological properties of the uterine muscle and uterine contractions. Since pregnancy is a long process, we decided to also characterize, for the first time, non-contraction intervals (dummy intervals) of the uterine records, i.e., EHG signals accompanied by a simultaneously recorded external tocogram measuring mechanical uterine activity (TOCO signal). For this purpose, we developed a new set of uterine records, TPEHGT DS, containing preterm and term uterine records of pregnant women, and uterine records of non-pregnant women. We quantitatively characterized contraction intervals (contractions) and dummy intervals of the uterine records of the TPEHGT DS in terms of the normalized power spectra of the EHG and TOCO signals, and developed a new method for predicting preterm birth. The results on the characterization revealed that the peak amplitudes of the normalized power spectra of the EHG and TOCO signals of the contraction and dummy intervals in the frequency band 1.0-2.2 Hz, describing the electrical and mechanical activity of the uterus due to the maternal heart (maternal heart rate), are high only during term pregnancies, when the delivery is still far away; and they are low when the delivery is close. However, these peak amplitudes are also low during preterm pregnancies, when the delivery is still supposed to be far away (thus suggesting the danger of preterm birth); and they are also low or barely present for non-pregnant women. We propose the values of the peak amplitudes of the normalized power spectra due to the influence of the maternal heart, in an electro-mechanical sense, in the frequency band 1.0-2.2 Hz as a new biophysical marker for the preliminary, or early, assessment of the danger of preterm birth. The classification of preterm and term, contraction and dummy intervals of the TPEHGT DS, for the task of the automatic prediction of preterm birth, using sample entropy, the median frequency of the power spectra, and the peak amplitude of the normalized power spectra, revealed that the dummy intervals provide quite comparable and slightly higher classification performances than these features obtained from the contraction intervals. This result suggests a novel and simple clinical technique, not necessarily to seek contraction intervals but using the dummy intervals, for the early assessment of the danger of preterm birth. Using the publicly available TPEHG DB database to predict preterm birth in terms of classifying between preterm and term EHG records, the proposed method outperformed all currently existing methods. The achieved classification accuracy was 100% for early records, recorded around the 23rd week of pregnancy; and 96.33%, the area under the curve of 99.44%, for all records of the database. Since the proposed method is capable of using the dummy intervals with high classification accuracy, it is also suitable for clinical use very early during pregnancy, around the 23rd week of pregnancy, when contractions may or may not be present.

Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

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.

Vaginal electrohysterography: the design and preliminary evaluation of a novel device for uterine contraction monitoring in an ovine model (.)

OBJECTIVE

Tocodynamometry is the most common method of labor evaluation but most clinicians would agree it has limited utility before 26 weeks of gestation. The obesity epidemic has further reduced our ability to accurately detect uterine contractions using the tocodynamometer at any gestational age. We sought to design and test a novel contraction monitor that bypasses the maternal abdomen.

METHODS

An optimized version of an intravaginal electrohysterographic ring device was tested in an ovine model. The device and its methodology as well as the tocodynamometer were validated against the current gold standard uterine activity monitor, the intrauterine pressure catheter in six sheep at varying gestational ages.

RESULTS

Both the intravaginal ring device and the tocodynamometer correlated well with IUPC, r = 0.69 and 0.73, respectively (p < 0.001). The number of contractions detected by each monitor remained similar even after accounting for confounders.

CONCLUSIONS

These results suggest that uterine activity can be monitored from the vaginal interface in an ovine model and offers an alternative clinical tool for the detection of contractions in situations, in which tocodynamometry would be ineffective or intrauterine monitoring inappropriate.

Feasibility and analysis of bipolar concentric recording of electrohysterogram with flexible active electrode

The conduction velocity and propagation patterns of the electrohysterogram (EHG) provide fundamental information on the electrophysiological condition of the uterus. However, the accuracy of these measurements can be impaired by both the poor spatial selectivity and sensitivity to the relative direction of the contraction propagation associated with conventional disc electrodes. Concentric ring electrodes could overcome these limitations. The aim of this study was to examine the feasibility of picking up surface EHG signals using a new flexible tripolar concentric ring electrode (TCRE), and to compare these signals with conventional bipolar recordings. Simultaneous recording of conventional bipolar signals and bipolar concentric EHG (BC-EHG) were carried out on 22 pregnant women. Signal bursts were characterized and compared. No significant differences were found between the channels in either duration or dominant frequency in the Fast Wave High frequency range. Nonetheless, the high pass filtering effect of the BC-EHG recordings gave lower frequency content between 0.1 and 0.2 Hz. Although the BC-EHG signal amplitude was about 5-7 times smaller than that of bipolar recordings, a similar signal-to-noise ratio was obtained. These results suggest that the flexible TCRE is able to pick up uterine electrical activity and could provide additional information for deducing the uterine electrophysiological condition.

Study protocol: PoPE-Prediction of Preterm delivery by Electrohysterography

Background

Traditional methods used for prediction of preterm delivery are subjective and inaccurate. The Electrohysterogram (EHG) and in particular the estimation of the EHG conduction velocity, is a relatively new promising method for detecting imminent preterm delivery. To date the analysis of the conduction velocity has relied on visual inspection of the signals. As a next step towards the introduction of EHG analysis as a clinical tool, we propose an automated method for EHG conduction velocity estimation for both the speed and direction of single spike propagation.

Methods/Design

The study design will be an observational cohort study. 100 pregnant women, gestational age between 23 + 5 and 34 weeks, admitted for threatening preterm labor or preterm prelabor rupture of membranes, will be included. The length of the cervical canal will be measured by transvaginal ultrasound. The EHG will be recorded using 4 electrodes in a fixed configuration. Contractions will be detected by analysis of the EHG and using an estimation of the intra uterine pressure. In the selected contractions, the delays between channels will be estimated by cross-correlation, and subsequently, the average EHG conduction velocity will be derived. Patients will be classified as labor group and non-labor group based on the time between measurement and delivery. The average conduction velocity and cervical length will be compared between the groups. The main study endpoints will be sensitivity, specificity, and area under the ROC curve for delivery within 1,2,4,7, and 14 days from the measurement.

Discussion

In this study, the diagnostic accuracy of EHG conduction velocity analysis will be evaluated for detecting preterm labor. Visual and automatic detection of contractions will be compared. Planar wave propagation will be assumed for the calculation of the CV vector.

Trial registration

Current Controlled Trials ISRCTN07603227.

Automated conduction velocity analysis in the electrohysterogram for prediction of imminent delivery: a preliminary study

Background. Analysis of the electrohysterogram (EHG) is a promising diagnostic tool for preterm delivery. For the introduction in the clinical practice, analysis of the EHG should be reliable and automated to guarantee reproducibility. Study Goal. Investigating the feasibility of automated analysis of the EHG conduction velocity (CV) for detecting imminent delivery. Materials and Methods. Twenty-two patients presenting with uterine contractions (7 preterm) were included. An EHG was obtained noninvasively using a 64-channel high-density electrode grid. Contractions were selected based on the estimated intrauterine pressure derived from the EHG, the tocodynamometer, and maternal perception. Within the selected contractions, the CV vector was identified in two dimensions. Results. Nine patients delivered within 24 hours and were classified as a labor group. 64 contractions were analyzed; the average amplitude of the CV vector was significantly higher for the labor group, 8.65 cm/s ± 1.90, compared to the nonlabor group, 5.30 cm/s ± 1.47 (P < 0.01). Conclusion. The amplitude of the CV is a promising parameter for predicting imminent (preterm) delivery. Automated estimation of this parameter from the EHG signal is feasible and should be regarded as an important prerequisite for future clinical studies and applications.

Characterization of EHG contractions at term labor by nonlinear analysis

Uterine electromyogram on the abdomen of pregnant women (electrohysterogram, EHG) plays an interesting role to evaluate possible risks to the binomial mother-fetus. In this sense, the present study explored the characterization of contractions by EHG during active phase of labor at term in a population at low risk. The goal was to investigate the differences in the contractions generated by women that evolve labor to a vaginal delivery (group 1) to those associated with caesarean section (group 2). Abdominal signals were acquired using Ag-AgCl electrodes in a bipolar configuration and the EHG was obtained by band-pass filtering in the range of 0.3 to 4 Hz. Sample entropy (SampEn) was used to calculate the irregularity of manually selected contractions of the EHG time series. The results showed that it is plausible to discriminate contractions from both groups as the average SampEn was 2.1359 with a standard deviation of 0.0583 for group 1 (N=8), while for group 2 (N=8) was 2.0352 with standard deviation of 0.0946; it was found significant statistical difference between groups as p was 0.046. Consequently, the nonlinear analysis via SampEn of EHG could provide an index to evaluate the quality of the active phase labor at term.

Effect of an oxytocin receptor antagonist (atosiban) on uterine electrical activity

OBJECTIVE

The purpose of this study was to investigate the effect of atosiban (Tractocile; Ferring, Limhamn, Sweden), an oxytocin receptor antagonist, on uterine electrical activity in women with preterm labor and to determine whether this information can assist in the prediction of preterm delivery.

STUDY DESIGN

Uterine electrical activity was recorded prospectively in 21 women with preterm labor before and during treatment with Tractocile and, for purpose of comparison, in 4 pregnant women without uterine contractions to set the baseline of uterine electrical activity in a quiescent uterus. Uterine activity was recorded with a noninvasive, 9-channel recorder with an electromyography amplifier and a 3-dimensional position sensor with an automatic data analyzer. Uterine electrical activity was quantified by an electrical uterine monitor (EUM) and measured in microwatts per second (μW/s).

RESULTS

The overall pre-Tractocile EUM index was 3.43 ± 0.58 μW/s, which was significantly higher than baseline uterine activity in women without preterm contractions (2.3 ± 0.11 μW/s; P = .001). During the administration of Tractocile, the EUM index gradually decreased in a relatively constant rate from 3.43 ± 0.58 μW/s to 2.56 ± 0.88 μW/s after 330 minutes of continuous therapy (P < .001). The peak effect of Tractocile was observed 4 hours after the initiation of treatment and was followed by a relative plateau. Women with a latency of <7 days from treatment to delivery were characterized by a distinct EUM-pattern in response to Tractocile, compared with women with a latency of ≥7 days (P < .001). A similar EUM-pattern after the administration of Tractocile was also observed for women who delivered at <37 weeks of gestation compared with the women who delivered at term.

CONCLUSION

Tractocile reduces uterine electrical activity in women with preterm labor. This information can provide more insight into the effects of tocolytic agents and to aid in the risk stratification of preterm delivery in women with preterm contractions.

Cross-correlation analysis of multichannel uterine EMG signals

The prevention of preterm labor remains one of the primary goals of obstetric research. One way to achieve this goal effectively is to understand the mechanisms regulating the uterine contractility. Herein, we evaluate the correlation between uterine electrical activities recorded from spatially-distributed regions by calculating the nonlinear regression coefficient. Results have shown that, during pregnancy, the degree of interdependence between signals is very high whereas, at labor, the correlation between the signals decreases remarkably. We conclude that pregnancy is characterized by the presence of few local potential sources dominating the other sources while at the onset of labor, the number of these sources increases remarkably which affects therefore the correlation between the signals.

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 performance of the frequency-related parameters derived from uterine EMG signals

Frequency-related parameters derived from the uterine electromyogram (EMG) signals are widely used in many pregnancy monitoring and preterm delivery prediction studies. Although they are classical parameters, they are well suited for quantifying uterine EMG signals and have many advantages over amplitude-related parameters. The present work aims to compare various frequency-related parameters according to their classification performances (pregnancy vs. labor) using the receiver operating characteristic (ROC) curve analysis. The comparison between the parameters indicates that median frequency is the best frequency-related parameter that can be used for distinguishing between pregnancy and labor contractions. We conclude that median frequency can be the representative frequency-related parameter for classification problems of uterine EMG.

Classification of multichannel uterine EMG signals

Classification of multichannel uterine electromyogram (EMG) signals is addressed. Signals were recorded by a matrix of 16 electrodes. First, signals corresponding to each channel were individually classified using an artificial neural network (ANN) based on radial basis functions (RBF). The results have shown that the classification performance varies from one channel to another. Then, a decision fusion method based on these classification performances was tested. After fusion, the network yielded better classification accuracy than any individual channel could provide. The high percentage of correctly classified labor/non-labor events proves the efficiency of multichannel recordings in detecting labor. These findings can be very useful for the aim of classifying antepartum versus labor patients.

Nonlinearity of EHG signals used to distinguish active labor from normal pregnancy contractions

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.

Noninvasive uterine electromyography for prediction of preterm delivery

OBJECTIVE

Power spectrum (PS) of uterine electromyography (EMG) can identify true labor. EMG propagation velocity (PV) to diagnose labor has not been reported. The objective was to compare uterine EMG against current methods to predict preterm delivery.

STUDY DESIGN

EMG was recorded in 116 patients (preterm labor, n = 20; preterm nonlabor, n = 68; term labor, n = 22; term nonlabor, n = 6). A Student t test was used to compare EMG values for labor vs nonlabor (P < .05, significant). Predictive values of EMG, Bishop score, contractions on tocogram, and transvaginal cervical length were calculated using receiver-operator characteristics analysis.

RESULTS

PV was higher in preterm and term labor compared with nonlabor (P < .001). Combined PV and PS peak frequency predicted preterm delivery within 7 days with area under the curve (AUC) of 0.96. Bishop score, contractions, and cervical length had an AUC of 0.72, 0.67, and 0.54.

CONCLUSION

Uterine EMG PV and PS peak frequency more accurately identify true preterm labor than clinical methods.

Noninvasive estimation of the electrohysterographic action-potential conduction velocity

Electrophysiological monitoring of the fetal-heart and the uterine-muscle activity, referred to as an electrohysterogram, is essential to permit timely treatment during pregnancy. While remarkable progress is reported for fetal-cardiac-activity monitoring, the electrohysterographic (EHG) measurement and interpretation remain challenging. In particular, little attention has been paid to the analysis of the EHG propagation, whose characteristics might be predictive of the preterm delivery. Therefore, this paper focuses, for the first time, on the noninvasive estimation of the conduction velocity of the EHG-action potentials. To this end, multichannel EHG recording and surface high-density electrodes are used. A maximum-likelihood method is employed for analyzing the EHG-action-potential propagation in two dimensions. The use of different weighting strategies of the derived cost function is introduced to deal with the poor signal similarity between different channels. The presented methods were evaluated by specific simulations proving the best weighting strategy to lead to an accuracy improvement of 56.7%. EHG measurements on ten women with uterine contractions confirmed the feasibility of the method by leading to conduction velocity values within the expected physiological range.

Complexity analysis of the uterine electromyography

In respect to the main goal of our ongoing work for predicting preterm birth, we analyze in this paper the complexity of the uterine electromyography (EMG) by using the sample entropy (SampEn) algorithm. By considering recent methodological developments, we measure the SampEn over multiple scales using the wavelet packet decomposition method. The results obtained from the analyzed data indicate that SampEn decreases along pregnancy. Furthermore, we demonstrate that the computed SampEn parameter may discriminate between the two classes (pregnancy/labor). The results are supported by statistical analysis using t-test indicating good statistical significance with a confidence level of 95%. A surrogate data test is also performed to investigate the nature of the underlying dynamics of our experimental data. The results are very promising for monitoring pregnancy and detecting labor to help identify 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.

Can myometrial electrical activity identify patients in preterm labor?

OBJECTIVE

The objective of the study was to determine whether myometrial electrical activity can differentiate false from true preterm labor.

STUDY DESIGN

Electrical uterine myography (EUM) was measured prospectively on 87 women, gestational age less than 35 weeks. The period between contractions, power of contraction peaks and movement of center of electrical activity (RMS), was used to develop an index score (1-5) for prediction of preterm delivery (PTD) within 14 days of the test. The score was compared with fetal fibronectin (fFN) and cervical length (CL).

RESULTS

Patients delivering within 14 days from testing showed a higher index and mean RMS (P = .000). No patients with EUM index scores of 1-2 delivered in this time frame. Combining EUM with CL or fFN increased predictability. Logistic regression revealed that history of PTD and EUM index had 4- to 5-fold increased risk for PTD. Gestational age at testing, body mass index, fFN, and CL were nonsignificant contributors to PTD risk.

CONCLUSION

Measuring myometrial electrical activity may enhance identification of patients in true premature labor.

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.