Application of wavelet coherence to the detection of uterine electrical activity synchronization in labor

Fresh evidence on connectedness between prominent markets during COVID-19 pandemic

Various empirical studies have examined the nexus between financial markets, but this study focused on the comovement among prominent markets. Our study examines the interrelationship among main financial markets, i.e., stock, oil, and commodity during the recent pandemic. The interconnections among the selected markets are investigated using a battery of wavelet coherence tools and the Granger causality test. From the wavelet coherence analysis, our findings indicate strong co-movements among the VIX, oil volatility, and commodity prices during pandemic and localized in all scales and over the sample period. The dependency strength among the considered economies is noted to increase in pandemic, which implies increased short- and long-term benefits for the investors. Moreover, Our result exhibits a feedback causality between OVIX and crude oil, VIX and S&P 500, and gasoline and VIX. Interestingly, a unidirectional causality exists between VIX and crude oil, S&P 500 and crude oil, Brent and crude oil, gasoline, crude oil, and VIX and OVIX. We advocate that the findings will be helpful for portfolio managers, investors, and officials around the world.

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.

Detection of preterm birth in electrohysterogram signals based on wavelet transform and stacked sparse autoencoder

Based on electrohysterogram, this paper designed a new method using wavelet-based nonlinear features and stacked sparse autoencoder for preterm birth detection. For each sample, three level wavelet decomposition of a time series was performed. Approximation coefficients at level 3 and detail coefficients at levels 1, 2 and 3 were extracted. Sample entropy of the detail coefficients at levels 1, 2, 3 and approximation coefficients at level 3 were computed as features. The classifier was constructed based on stacked sparse autoencoder. In addition, stacked sparse autoencoder was further compared with extreme learning machine and support vector machine in relation to their classification performance of electrohysterogram. The experiment results reveal that classifier based on stacked sparse autoencoder showed better performance than the other two classifiers with an accuracy of 90%, a sensitivity of 92%, a specificity of 88%. The results indicate that the method proposed in this paper could be effective for detecting preterm birth in electrohysterogram and the framework designed in this work presents higher discriminability than other techniques.

EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome

The brain is a large-scale complex network often referred to as the “connectome”. Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB (Math works, inc), and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources. It includes i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is the unique tool that combines the M/EEG functional connectivity analysis and the computation of network measures derived from the graph theory. The first version of EEGNET is easy to use, flexible and user friendly. EEGNET is an open source tool and can be freely downloaded from this webpage: https://sites.google.com/site/eegnetworks/.

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.

Improving the classification rate of labor vs. normal pregnancy contractions by using EHG multichannel recordings

Most of the studies on the synchronization between EHG signals, recorded during the same contractions at different locations, are limited to the use of only two channels. Multichannel techniques have however been widely applied to EEG signals but rarely to EHG. In this paper, we investigate the use of multichannel uterine EMG signals for classifying contractions. We compare the performance of phase synchronization in distinguishing between labor and normal pregnancy contractions by using either only two channels or a 4x4 matrix positioned on the woman's abdomen. We used two indexes to measure the phase synchronization: mean phase coherence and phase entropy. ROC curves indicate that the use of multichannel signals can significantly improve the classification rate of pregnancy and labor contractions.