Performance comparison of coupling-evaluation methods in discriminating between pregnancy and labor EHG signals.

BACKGROUND: Recent years have seen an increased interest in electrohysterogram (EHG) signals as a means to evaluate the synchronization of uterine contractions. Several studies have pointed out that the quality of signal processing - and hence the interpretation of measurement results - is affected significantly by the choice of measurement technique and the presence of non-stationary frequency content in EHG signals. To our knowledge, the effect of time variance on the quality of EHG signal processing has never been fully investigated. How best to process EHG signals with the goal of distinguishing labor-induced contractions from their harmless, pre-labor cousins, remains an open question. METHOD: Our methodology is based on three pillars. The first consists of a new method for EHG preprocessing in which we apply a second-order Butterworth filter to retain only the EHG fast-wave, low-frequency band (FWL), then use a bivariate piecewise stationary pre-segmentation (bPSP) algorithm to segment the EHG signal into stationary parts. The second pillar addresses the estimation of connectivity and directionality using three methods: nonlinear correlation coefficient (h2), general synchronization (H), and Granger causality (GC). The third pillar is related to signal classification and discrimination between pregnancy and labor using receiver operating curves (ROC) and connectivity and direction maps. For this purpose, we analyze the impact of four factors on data processing efficiency: i) method of connectivity detection, ii) effect of piecewise stationary segmentation preprocessing, iii) retained frequency content and iv) electrode configuration used for EHG recording (bipolar vs. unipolar). RESULTS: Our results show that piecewise signal segmentation and filtering considerably improves classification performance and statistical significance for some connectivity methods, in particular the h2. To this end we propose a new approach (detailed below) for h2 called Filtered-Windowed (FW) h2 that better highlights the differences between pregnancy and labor in the connectivity matrix and directionality maps. CONCLUSIONS: This is the first comparative study of the effects of multiple processing factors on connectivity measurement efficiency. Our results indicate that appropriate preprocessing can improve the differentiation of pregnancy and labor-induced contraction signals and may lead to innovative applications in the prevention of preterm labor.

Fentanyl in a pectin gel treating breakthrough pain in vertebral compression fracture due to multiple myeloma: A descriptive study of three cases.

Breakthrough pain (BTP) currently represents a challenge for health professionals dedicated to the treatment of pain. In this descriptive 1-year follow-up study on three patients with BTP from vertebral crush, in the context of multiple myeloma, the authors have observed the great either efficacy or tolerability profile of fentanyl pectin nasal spray. The most relevant findings in this study were better adherence to treatment compared to previously opioids and also great personal satisfaction. Because of common pathophysiological mechanism for noncancerous pain of bone origin, these good results could open the door to investigation of the use of this drug in this patient's group.

The Icelandic 16-electrode electrohysterogram database.

External recordings of the electrohysterogram (EHG) can provide new knowledge on uterine electrical activity associated with contractions. Better understanding of the mechanisms underlying labor can contribute to preventing preterm birth which is the main cause of mortality and morbidity in newborns. Promising results using the EHG for labor prediction and other uses in obstetric care are the drivers of this work. This paper presents a database of 122 4-by-4 electrode EHG recordings performed on 45 pregnant women using a standardized recording protocol and a placement guide system. The recordings were performed in Iceland between 2008 and 2010. Of the 45 participants, 32 were measured repeatedly during the same pregnancy and participated in two to seven recordings. Recordings were performed in the third trimester (112 recordings) and during labor (10 recordings). The database includes simultaneously recorded tocographs, annotations of events and obstetric information on participants. The publication of this database enables independent and novel analysis of multi-electrode EHG by the researchers in the field and hopefully development towards new life-saving technology.

Effect of filtering on the classification rate of nonlinear analysis methods applied to uterine EMG signals.

Nonlinear time series analysis can provide useful information regarding nonlinear features of biological signals. The effect of filtering on the performance of nonlinear methods is not well-understood. In this work, we investigate the effects of signal filtering on the sensitivity of four nonlinear methods: Time reversibility, Sample Entropy, Lyapunov Exponents and Delay Vector Variance. These methods were applied to uterine EMG signals with the aim of using them to discriminate between pregnancy and labor contractions. The signals were filtered using three different band-pass filters before the application of the methods. Results showed that the sensitivity of some methods such as sample entropy was significantly improved with filtering. On the other hand, filtering had little effect on some other methods such as time reversibility. This study concludes that while filtering increases computation time, it may be necessary for some nonlinear methods particularly those with low sensitivity.

Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals.

Several measures have been proposed to detect nonlinear characteristics in time series. Results on time series, multiple surrogates and their z-score are used to statistically test for the presence or absence of non-linearity. The z-score itself has sometimes been used as a measure of nonlinearity. The sensitivity of nonlinear methods to the nonlinearity level and their robustness to noise have rarely been evaluated in the past. While surrogates are important tools to rigorously detect nonlinearity, their usefulness for evaluating the level of nonlinearity is not clear. In this paper we investigate the performance of four methods arising from three families that are widely used in non-linearity detection: statistics (time reversibility), predictability (sample entropy, delay vector variance) and chaos theory (Lyapunov exponents). We used sensitivity to increasing complexity and the mean square error (MSE) of Monte Carlo instances for quantitative comparison of their performances. These methods were applied to a Henon nonlinear synthetic model in which we can vary the complexity degree (CD). This was done first by applying the methods directly to the signal and then using the z-score (surrogates) with and without added noise. The methods were then applied to real uterine EMG signals and used to distinguish between pregnancy and labor contraction bursts. The discrimination performances were compared to linear frequency based methods classically used for the same purpose such as mean power frequency (MPF), peak frequency (PF) and median frequency (MF). The results show noticeable difference between different methods, with a clear superiority of some of the nonlinear methods (time reversibility, Lyapunov exponents) over the linear methods. Applying the methods directly to the signals gave better results than using the z-score, except for sample entropy.

Comparing three devices for jump height measurement in a heterogeneous group of subjects.

The purpose of this study was to compare the accuracy of 3 devices for measuring jump heights of subjects with a range of abilities. The methods used were an accelerometer-based technique (KineJump), a contact mat (Newtest Powertimer 300 series), and the Vertec measurement device. The reference method used was a video analysis technique. Data were collected simultaneously with all 4 methods. Of particular interest was to evaluate the accuracy of the accelerometer device (KineJump) because it has not been systematically compared with other methods in the previous literature. Participants (N = 101) varied from well-trained athletes to sedentary individuals and their ages ranging from 18 to 70 years old. From the statistical analysis, it can be concluded that KineJump has accuracy that is at least as good as the accuracy of the Vertec and contact mat, although the overall accuracy of all the methods was disappointing at typically ± 5 cm. The 2 methods that rely on a time-of-flight calculation of the jump height (contact mat and accelerometer) showed systematically lower values than the other 2 methods. None of the physical parameters showed significant correlation with the measurement error made by the methods, with the exception of a slight positive correlation of height, leg length, and foot length to the error of the Vertec method. Linear regression on the Bland-Altman plots did not reveal a significant correlation between the jump height and the error of a method, except for the Vertec. The 3 jump height measurement devices can be used to evaluate performance in a diverse group of people, but considerable deviation from actual jump height is apparent, depending on the method of calculation.

Windowed multivariate autoregressive model improving classification of labor vs. pregnancy contractions.

Analyzing the propagation of uterine electrical activity is poised to become a powerful tool in labor detection and for the prediction of preterm labor. Several methods have been proposed to investigate the relationship between signals recorded externally from several sites on the pregnant uterus. A promising recent method is the multivariate autoregressive (MVAR) model. In this paper we proposed a windowed (time varying) version of the multivariate autoregressive model, called W-MVAR, to investigate the connectivity between signals while still respecting their non-stationary characteristics. The proposed method was tested on synthetic signals as well as applied to real signals. The comparison between the two methods on synthetic signals showed the superiority of W-MVAR to detect connectivity even if it is non-stationary. The application of W-MVAR on multichannel real uterine signals show that the proposed method is a good tool to distinguish non-labor and labor signals. These results are very promising and can very possibly have important clinical applications in labor detection and preterm labor prediction.

A multiscale model of the electrohysterogram the BioModUE_PTL project.

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

Bivariate piecewise stationary segmentation; improved pre-treatment for synchronization measures used on non-stationary biological signals.

Analysis of synchronization between biological signals can be helpful in characterization of biological functions. Many commonly used measures of synchronicity assume that the signal is stationary. Biomedical signals are however often strongly non stationary. We propose to use a bivariate piecewise stationary pre-segmentation (bPSP) of the signals of interest, before the computation of synchronization measures on biomedical signals to improve the performance of standard synchronization measures. In prior work we have shown how this can be achieved by using the auto-spectrum of either one of the signals under investigation. In this work we show how major improvements of the performance of synchronization measures can be achieved using the cross-spectrum of the signals to detect stationary changes which occur independently in either signal. We show on synthetic as well as on real biological signals (epileptic EEG and uterine EMG) that the proposed bPSP approach increases the accuracy of the measures by making a good tradeoff between the stationarity assumption and the length of the analyzed segments, when compared to the classical windowing method.

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.

Combination of canonical correlation analysis and empirical mode decomposition applied to denoising the labor electrohysterogram.

The electrohysterogram (EHG) is often corrupted by electronic and electromagnetic noise as well as movement artifacts, skeletal electromyogram, and ECGs from both mother and fetus. The interfering signals are sporadic and/or have spectra overlapping the spectra of the signals of interest rendering classical filtering ineffective. In the absence of efficient methods for denoising the monopolar EHG signal, bipolar methods are usually used. In this paper, we propose a novel combination of blind source separation using canonical correlation analysis (BSS_CCA) and empirical mode decomposition (EMD) methods to denoise monopolar EHG. We first extract the uterine bursts by using BSS_CCA then the biggest part of any residual noise is removed from the bursts by EMD. Our algorithm, called CCA_EMD, was compared with wavelet filtering and independent component analysis. We also compared CCA_EMD with the corresponding bipolar signals to demonstrate that the new method gives signals that have not been degraded by the new method. The proposed method successfully removed artifacts from the signal without altering the underlying uterine activity as observed by bipolar methods. The CCA_EMD algorithm performed considerably better than the comparison methods.

Comparison between approximate entropy, correntropy and time reversibility: application to uterine electromyogram signals.

Detection of nonlinearity should be the first step before any analysis of nonlinearity or nonlinear behavior in biological signal. The question is which method should be used in each case and which one can best respect the different characteristics of the signals under investigation. In this paper we compare three methods widely used in nonlinearity detection: approximate entropy, correntropy and time reversibility. The false alarm rates with the numbers of surrogates for the three methods were computed on linear, nonlinear stationary and nonlinear nonstationary signals. The results indicate the superiority of time reversibility over the other methods for detecting linearity and nonlinearity in different signal types. The application of time reversibility on uterine electromyographic signal showed very good performance in classifying pregnancy and labor signals.

Uterine electromyogram database and processing function interface: An open standard analysis platform for electrohysterogram signals.

The uterine electromyogram or electrohysterogram (EHG) is one of the most promising biophysical markers of preterm labor. At this time no recording parameter standard exists for EHG recordings which can be a problem for the establishment of international multicentric trials. In this paper, we present a management and processing system dedicated to storing and processing EHG signals. This system can process EHG signals recorded in different experimental conditions i.e. different sampling frequencies. The signal management is performed using an easy to use graphical user interface. Other available functions include visualization, preprocessing and analysis of EHG signals. The proposed processing functions provide temporal, spectral and time-scale parameters obtained from the EHG bibliography. The obtained results from real signals recorded in two different hospitals in two different countries are in accordance with the literature and demonstrate the potential of the proposed system. The incorporation of new functions is easy, due to a standardization of the EHG data formats.