The contractiongram: A method for the visualization of uterine contraction evolution using the electrohysterogram

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The uterine electromyogram, also called electrohysterogram (EHG), is the electrical signal generated by uterine contractile activity. The EHG has been considered an expanding technique for pregnancy monitoring and preterm risk evaluation. Data were collected on the abdominal surface. It has been speculated the effect of the placenta location on the characteristics of the EHG. In this work, a preliminary exploration method is proposed using the average spectra of Alvarez waves contractions of subjects with anterior and non-anterior placental position as a basis for the triple-dispersion Cole model that provides a best fit for these two cases. This leads to the uterine impedance estimation for these two study cases. Non-linear least square fitting (NLSF) was applied for this modelling process, which produces electric circuit fractional models’ representations. A triple-dispersion Cole-impedance model was used to obtain the uterine impedance curve in a frequency band between 0.1 and 1 Hz. A proposal for the interpretation relating the model parameters and the placental influence on the myometrial contractile action is provided. This is the first report regarding in silico estimation of the uterine impedance for cases involving anterior or non-anterior placental positions.

Section: Methodsmentioning

confidence: 99%

“…The selection criteria for this electrode arrangement can be found in Esgalhado et al (2020) [46]. A uterine contraction detector, previously developed under the Uterine Explorer project [46][47][48][49], [50] processed the mentioned database. The obtained contraction data pool was, subsequently, the input for an unsupervised clustering classifier.…”

Section: Methodsmentioning

confidence: 99%

A Preliminary Exploration of the Placental Position Influence on Uterine Electromyography Using Fractional Modelling

Şan

Batista

Russo

et al. 2022

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“…To profile EHG signals for preterm labor prediction, a wide range of temporal and spectral measurements have been investigated [14][15][16]. Khalil and Duchene [17] presented a hybrid algorithm based on the dynamic cumulative sum and multiscale decomposition to detect different events of EHG signals.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Preterm Delivery from Unbalanced EHG Database

Far

Beiramvand

Shahbakhti

et al. 2022

Objective: The early prediction of preterm labor can significantly minimize premature delivery complications for both the mother and infant. The aim of this research is to propose an automatic algorithm for the prediction of preterm labor using a single electrohysterogram (EHG) signal. Method: The proposed method firstly employs empirical mode decomposition (EMD) to split the EHG signal into two intrinsic mode functions (IMFs), then extracts sample entropy (SampEn), the root mean square (RMS), and the mean Teager–Kaiser energy (MTKE) from each IMF to form the feature vector. Finally, the extracted features are fed to a k-nearest neighbors (kNN), support vector machine (SVM), and decision tree (DT) classifiers to predict whether the recorded EHG signal refers to the preterm case. Main results: The studied database consists of 262 term and 38 preterm delivery pregnancies, each with three EHG channels, recorded for 30 min. The SVM with a polynomial kernel achieved the best result, with an average sensitivity of 99.5%, a specificity of 99.7%, and an accuracy of 99.7%. This was followed by DT, with a mean sensitivity of 100%, a specificity of 98.4%, and an accuracy of 98.7%. Significance: The main superiority of the proposed method over the state-of-the-art algorithms that studied the same database is the use of only a single EHG channel without using either synthetic data generation or feature ranking algorithms.

“…To overcome these problems, recent studies have emerged [ 7 , 8 , 9 , 10 ] regarding the electrohysterogram (EHG) potential for applications in preterm and term birth prediction. The EHG, also called uterine electromyogram, consists of non-invasively recording the electrical signal generated by the contractile activity of the uterus [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the analysis of this bioelectrical signal is useful for the general monitoring of uterine activity during pregnancy. The EHG also contributes to advances in uterine electrophysiology [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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

Martins

Batista

Mouriño

et al. 2022

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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).