Electrohysterographic Signals Research Articles

Automatic semantic segmentation of EHG recordings by deep learning: An approach to a screening tool for use in clinical practice

Background and ObjectivePreterm delivery is an important factor in the disease burden of the newborn and infants worldwide. Electrohysterography (EHG) has become a promising technique for predicting this condition, thanks to its high degree of sensitivity. Despite the technological progress made in predicting preterm labor, its use in clinical practice is still limited, one of the main barriers being the lack of tools for automatic signal processing without expert supervision, i.e. automatic screening of motion and respiratory artifacts in EHG records. Our main objective was thus to design and validate an automatic system of segmenting and screening the physiological segments of uterine origin in EHG records for robust characterization of uterine myoelectric activity, predicting preterm labor and help to promote the transferability of the EHG technique to clinical practice. MethodsFor this, we combined 300 EHG recordings from the TPEHG DS database and 69 EHG recordings from our own database (Ci2B-La Fe) of women with singleton gestations. This dataset was used to train and evaluate U-Net, U-Net++, and U-Net 3+ for semantic segmentation of the physiological and artifacted segments of EHG signals. The model's predictions were then fine-tuned by post-processing. ResultsU-Net 3+ outperformed the other models, achieving an area under the ROC curve of 91.4 % and an average precision of 96.4 % in detecting physiological activity. Thresholds from 0.6 to 0.8 achieved precision from 93.7 % to 97.4 % and specificity from 81.7 % to 94.5 %, detecting high-quality physiological segments while maintaining a trade-off between recall and specificity. Post-processing improved the model's adaptability by fine-tuning both the physiological and corrupted segments, ensuring accurate artifact detection while maintaining physiological segment integrity in EHG signals. ConclusionsAs automatic segmentation proved to be as effective as double-blind manual segmentation in predicting preterm labor, this automatic segmentation tool fills a crucial gap in the existing preterm delivery prediction system workflow by eliminating the need for double-blind segmentation by experts and facilitates the practical clinical use of EHG. This work potentially contributes to the early detection of authentic preterm labor women and will allow clinicians to design individual patient strategies for maternal health surveillance systems and predict adverse pregnancy outcomes.

Optimizing Uterine Synchronization Analysis in Pregnancy and Labor Through Window Selection and Node Optimization

1) Introduction: Preterm labor (PL) has globally become the leading cause of death in children under the age of 5 years. One of the most significant keys to preventing preterm labor is its early detection. 2) Objectives: The primary objectives of this study are to address the problem of PL by providing a new approach by analyzing the electrohysterographic (EHG) signals, which are recorded on the mother's abdomen during labor and pregnancy. 3) Methods: The EHG signal reflects the electrical activity that induces the mechanical contraction of the myometrium. Because EHGs are known to be non-stationary signals, and because we anticipate connectivity to alter during contraction (due to electrical diffusion and the mechanotransduction process), we applied the windowing approach on real signals to identify the best windows and the best nodes with the most significant data to be used for classification. The suggested pipeline includes: i) dividing the 16 EHG signals that are recorded from the abdomen of pregnant women in N windows; ii) apply the connectivity matrices on each window; iii) apply the Graph theory-based measures on the connectivity matrices on each window; iv) apply the consensus Matrix on each window in order to retrieve the best windows and the best nodes. Following that, several neural network and machine learning methods are applied to the best windows and best nodes to categorize pregnancy and labor contractions, based on the different input parameters (connectivity method alone, connectivity method plus graph parameters, best nodes, all nodes, best windows, all windows). 4) Results: Results showed that the best nodes are nodes 8, 9, 10, 11, and 12; while the best windows are 2, 4, and 5. The classification results obtained by using only these best nodes are better than when using the whole nodes. The results are always better when using the full burst, whatever the chosen nodes. 5) Conclusion: The windowing approach proved to be an innovative technique that can improve the differentiation between labor and pregnancy EHG signals.

Fluctuation Analysis of Uterine Contractions in Term Pregnancies Using Electrohysterography Signals and Empirical Mode Decomposition-Based Multifractal Features

Investigating the fluctuations of uterine contractions is an indispensable diagnostic practice to evaluate the onset of labor. This work aims to differentiate the fluctuations associated with Term (gestation ≥ 37 weeks) pregnancies during varied gestational ages using electrohysterography (EHG) signals. The signals in the second and third trimesters are subjected to Multifractal-Empirical Mode Decomposition-based Detrended Fluctuation Analysis (MF-EMDDFA). Multifractal spectrum is estimated and features namely, Holder exponents ([Formula: see text], [Formula: see text]), broadness (BD), root slopes (LS, RS) and tangential slopes (TL, TR) are extracted. Machine learning methods, such as Naïve Bayes, adaptive boosting and random forest (RF) classifiers, are utilized to discriminate the contractions during different weeks of gestation. Results show that five multifractal features, namely [Formula: see text], [Formula: see text], BD, LS and TL, show significant differences between the considered gestational ages. An increase in regularity of the uterine contractions is observed as the gestational age increases. These features along with the RF classifier provide an accuracy, sensitivity, precision, recall and F1-score of greater than 98.30%. As the differentiation of uterine contractions during different weeks of gestation is vital to understanding the pregnancy progress, the proposed multifractal features could be used as potential biomarkers in clinical practices.

Automatic detection and characterization of uterine contraction using Electrohysterography

Preterm birth is the leading cause of perinatal morbidity and mortality. In clinical practice, the information of uterine contraction is an important reference for preterm delivery. The commonly used tocography method for detecting uterine contractions has low sensitivity and is not suitable for long-term measurement. The electrohysterography (EHG) can record the electrical activity of uterine muscle cells associated with contractions through electrodes on the abdomen. Therefore, this paper proposes a new automatic detection method of uterine contractions based on EHG signals. Specifically, utilizing the nonlinear property of entropy analysis, contraction and spike noise are distinguished in the complexity domain to highlight contraction activity. Then, the location of the contraction is detected by an adaptive thresholding method. Finally, the detected contractions are analyzed for pregnancy and labor. The results show that compared with the existing root mean square methods, the proposed method is less affected by spike noise, and the contraction detection rate on the Icelandic EHG database reaches 87.9%. Meanwhile, features extracted from detected contractions have significant differences between non-labor and labor categories. This demonstrates the feasibility of contraction detection by EHG signal, which can contribute to better care for high-risk pregnant women.

Multi-channel electrohysterography enabled uterine contraction characterization and its effect in delivery assessment

Uterine contractions are routinely monitored by tocodynamometer (TOCO) at late stage of pregnancy to predict the onset of labor. However, TOCO reveals no information on the synchrony and coherence of contractions, which are important contributors to a successful delivery. The electrohysterography (EHG) is a recording of the electrical activities that trigger the local muscles to contract. The spatial–temporal information embedded in multiple channel EHG signals make them ideal for characterizing the synchrony and coherence of uterine contraction. To proceed, contractile time-windows are identified from TOCO signals and are then used to segment out the simultaneously recorded EHG signals of different channels. We construct sample entropy SamEn and Concordance Correlation based feature ψ from these EHG segments to quantify the synchrony and coherence of contraction. To test the effectiveness of the proposed method, 122 EHG recordings in the Icelandic EHG database were divided into two groups according to the time difference between the gestational ages at recording and at delivery (TTD). Both SamEn and ψ show clear difference in the two groups (p<10−5) even when measurements were made 120 h before delivery. Receiver operating characteristic curve analysis of these two features gave AUC values of 0.834 and 0.726 for discriminating imminent labor defined with TTD ≤ 24 h. The SamEn was significantly smaller in women (0.1433) of imminent labor group than in women (0.3774) of the pregnancy group. Using an optimal cutoff value of SamEn to identify imminent labor gives sensitivity, specificity, and accuracy as high as 0.909, 0.712 and 0.743, respectively. These results demonstrate superiority in comparing to the existing SOTA methods. This study is the first research work focusing on characterizing the synchrony property of contractions from the electrohysterography signals. Despite the very limited dataset used in the validation process, the promising results open a new direction to the use of electrohysterography in obstetrics.

Fetal Health Assessment Through Remote Fetal Phonocardiography and Electrohysterography: Wearable Wireless Device Using Shakti Processor

Pregnant women and neonates are the most affected demographic group by the shortfall of accessible healthcare. In this work, we aimed to improve obstetric facilities in rural India by proposing a remote maternal health monitoring device in which pregnant women can avail healthcare services without long-distance travel. We designed and developed a prototype of the proposed device using the open-source Shakti processor to provide remote real-time monitoring of the fetal heart rate and uterine contractions for the detection of any abnormalities. Fetal phonocardiography signal was picked up using an acoustic sensor while the electrohysterography signal was acquired using surface electrodes placed on a pregnant woman. The placement of the sensor and electrodes was referred by an obstetrician. The system was placed on 10 pregnant women in their last trimester with their informed consent and the monitored parameters were validated by a clinician. The obtained fetal heart rate and uterine contractions were compared with that from standard clinical devices and the results from the proposed device were observed to be comparable with that of the standard devices.

Novel Multichannel Entropy Features and Machine Learning for Early Assessment of Pregnancy Progression Using Electrohysterography.

Preterm birth is the leading cause of morbidity and mortality involving over 10% of infants. Tools for timely diagnosis of preterm birth are lacking and the underlying physiological mechanisms are unclear. The aim of the present study is to improve early assessment of pregnancy progression by combining and optimizing a large number of electrohysterography (EHG) features with a dedicated machine learning framework. A set of reported EHG features are extracted. In addition, novel cross and multichannel entropy and mutual information are employed. The optimal feature set is selected using a wrapper method according to the accuracy of the leave-one-out cross validation. An annotated database of 74 EHG recordings in women with preterm contractions was employed to test the ability of the proposed method to recognize the onset of labor and the risk of preterm birth. Difference between using the contractile segments only and the whole EHG signal was compared. The proposed method produces an accuracy of 96.4% and 90.5% for labor and preterm prediction, respectively, much higher than that reported in previous studies. The best labor prediction was observed with the contraction segments and the best preterm prediction achieved with the whole EHG signal. Entropy features, particularly the newly-employed cross entropy contribute significantly to the optimal feature set for both labor and preterm prediction. Our results suggest that changes in the EHG, particularly the regularity, might manifest early in pregnancy. Single-channel and cross entropy may therefore provide relevant prognostic opportunities for pregnancy monitoring.

Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data.

Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models’ real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 ± 4.6%, average precision of 84.5 ± 11.7%, maximum F1-score of 79.6 ± 13.8%, and recall of 89.8 ± 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics.

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.

Identification of Contractions from Electrohysterography for Prediction of Prolonged Labor.

The analysis of the uterine electrical activity and its propagation patterns could potentially predict the risk of prolonged/arrested progress of labor. In our study, the Electrohysterography (EHG) signals of 83 participants in labor at around 3-4 cm of cervical dilatation, were recorded for about 30 minutes each. These signals were analyzed for predicting prolonged labor. Out of the 83 participants, 70 participants had normal progress of labor and delivered vaginally. The remaining 13 participants had prolonged/ arrested progress of labor and had to deliver through a cesarean section. In this paper, we propose an algorithm to identify contractions from the acquired EHG signals based on the energy of the signals. The role of contraction consistency and fundal dominance was evaluated for impact on progress of the labor. As per our study, the correlation of contractions was higher in case of normal progress of labor. We also observed that the upper uterine segment was dominant in cases with prolonged/arrested progress of labor.

Uterine Synchronization Analysis During Pregnancy and Labor Using Graph Theory, Classification Based on Neural Network and Deep Learning

1) ObjectivesPreterm birth caused by preterm labor is one of the major health problems in the world. In this article, we present a new framework for dealing with this problem through the processing of electrohysterographic signals (EHG) that are recorded during labor and pregnancy. The objective in this research is to improve the classification between labor and pregnancy contractions by using a new approach that focuses on the connectivity analysis based on graph parameters, representative of uterine synchronization, and comparing neural network and machine learning methods in order to classify between labor and pregnancy. 2) Material and methodsafter denoising of the 16 EHG signals recorded from pregnant women abdomen, we applied different connectivity methods to obtain connectivity matrices; then by using the graph theory, we extracted some graph parameters from the connectivity matrices; finally, we tested different neural network and machine learning methods on the features obtained from both graph and connectivity methods in order to classify between labor and pregnancy. 3) ResultsThe best results were obtained by using the logistic regression method. We also evidence the power of graph parameters extracted from the connectivity matrices to improve the classification results. 4) ConclusionThe use of graph analysis associated with machine learning methods can be a powerful tool to improve labor and pregnancy classification based on the analysis of EHG signals.

Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals.

One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.

Protocolo para captura de señales electromiográficas producidas por músculo uterino en gestantes para predicción de parto pretérmino

Objetivo: diseñar protocolo de proceso de captura de señales electrohisterográficas, para predicción de parto pretérmino. Materiales y métodos: estudio exploratorio – descriptivo, propositivo. Para el diseño se realizó revisión documental de artículos científicos investigativos de corte cualitativo y cuantitativo, en inglés y español, consultados en fuentes de información biomédica especializados como el MEsH y DeCS y en bases de datos como: PubMed, Scielo, IEEE, entre otros. Las expresiones de búsqueda fueron: electromiografía, músculo uterino y protocolo. No se encontraron protocolos específicos para electrohisterografía, por la cual se tomaron publicaciones referentes a protocolos de electromiografía en general, encontrándose un total de 193 publicaciones, de los cuales se seleccionaron 23, publicados entre 2012 y 2020. Para el análisis se utilizó el método de investigación denominado revisión integradora. Resultados: el protocolo plantea criterios que deben tenerse en cuenta antes y durante el procedimiento, equipos y materiales requeridos y contraindicaciones, para la captura de registro de señales electrohisterográficas. Conclusión: los protocolos de atención en salud son fundamentales, ya que por medio de ellos se estandarizan los procesos que se deben seguir en la captura de señales, minimizando las posibles complicaciones durante y después del procedimiento, en beneficio del binomio madre-hijo.

Automated detection of preterm condition using uterine electromyography based topological features

Accurate prediction of preterm birth is a global, public health priority. This necessitates the need for an efficient technique that aids in early diagnosis. The objective of this study is to develop an automated system for an effective detection of preterm (weeks of gestation < 37) condition using Electrohysterography (EHG) and topological features associated with the frequency components of signals. The EHG signals recorded prior to gestational age of 26 weeks are considered. The pre-processed signals are subjected to discrete Fourier transform to obtain the Fourier coefficients. The envelope is computed from the boundary of the complex Fourier coefficients identified using the α-shape method. Topological features namely, area, perimeter, circularity, convexity, ellipse variance and bending energy are extracted from the envelope. Classifications based on threshold-determination method and machine learning algorithms namely, naïve Bayes, decision tree and random forest are employed to differentiate the term and preterm conditions. The results show that the Fourier coefficients of EHG signals exhibit different shapes in the term and preterm conditions. The regularity of signals is found to increase in preterm condition. All the features are found to have significant differences between these two conditions. Bending energy as a single biomarker achieves a maximum accuracy of 80.7%. The random forest model based on the topological features detects the conditions with the maximum accuracy and positive predictive value of about 98.6%. Therefore, the proposed automated system seems to be effective and could be used for the accurate detection of term and preterm conditions.

Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios.

Electrohysterography (EHG) has been shown to provide relevant information on uterine activity and could be used for predicting preterm labor and identifying other maternal fetal risks. The extraction of high-quality robust features is a key factor in achieving satisfactory prediction systems from EHG. Temporal, spectral, and non-linear EHG parameters have been computed to characterize EHG signals, sometimes obtaining controversial results, especially for non-linear parameters. The goal of this work was to assess the performance of EHG parameters in identifying those robust enough for uterine electrophysiological characterization. EHG signals were picked up in different obstetric scenarios: antepartum, including women who delivered on term, labor, and post-partum. The results revealed that the 10th and 90th percentiles, for parameters with falling and rising trends as labor approaches, respectively, differentiate between these obstetric scenarios better than median analysis window values. Root-mean-square amplitude, spectral decile 3, and spectral moment ratio showed consistent tendencies for the different obstetric scenarios as well as non-linear parameters: Lempel–Ziv, sample entropy, spectral entropy, and SD1/SD2 when computed in the fast wave high bandwidth. These findings would make it possible to extract high quality and robust EHG features to improve computer-aided assessment tools for pregnancy, labor, and postpartum progress and identify maternal fetal risks.

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.

Accurate diagnosis of term–preterm births by spectral analysis of electrohysterography signals

Preterm delivery contributes to an increased risk of fetal and maternal death as well as several health deficiencies, thereby requiring special care and treatment that result in high financial costs. It is therefore of key importance to diagnose preterm delivery in advance in order to avoid or minimize its undesirable consequences.This paper proposes a novel method for non-invasive diagnosis of preterm delivery based on the classification of electrohysterography (EHG) signals. First, the EHG signal, which is related to the electrical activity of uterine muscles is recorded from the maternal fundus using surface electrodes. Then, the signal is sliced into frames for spectral analysis. Next, spectral analyses of the individual EHG signal frames are carried out and centroid frequencies of the frames are computed, establishing the elements of a feature vector that represents the time-varying spectral content of the EHG signal. Finally, this feature vector is employed for the classification of the underlying EHG signal for term–preterm diagnosis.The efficiency of the proposed approach is evaluated and compared with representative methods from the literature. Our results demonstrate that the proposed approach exhibits superior performance over other methods.

New electrohysterogram-based estimators of intrauterine pressure signal, tonus and contraction peak for non-invasive labor monitoring

Background: Uterine activity monitoring is an essential part of managing the progress of pregnancy and labor. Although intrauterine pressure (IUP) is the only reliable method of estimating uterine mechanical activity, it is highly invasive. Since there is a direct relationship between the electrical and mechanical activity of uterine cells, surface electrohysterography (EHG) has become a noninvasive monitoring alternative. The Teager energy (TE) operator of the EHG signal has been used for IUP continuous pressure estimation, although its accuracy could be improved. We aimed to develop new optimized IUP estimation models for clinical application. Approach: We first considered enhancing the optimal estimation of IUP clinical features (maximum pressure and tonus) rather than optimizing the signal only (continuous pressure). An adaptive algorithm was also developed to deal with inter-patient variability. For each optimizing signal feature (continuous pressure, maximum pressure and tonus), individual (single patient), global (full database) and adaptive models were built to estimate the recorded IUP signal. The results were evaluated by computing the root mean square errors (RMSe): continuous pressure error (CPe), maximum pressure error (MPe) and tonus error (TOe). Main results: The continuous pressure global model yielded IUP estimates with Cpe = 14.61 mm Hg, MPe = 29.17 mm Hg and Toe = 7.8 mm Hg. The adaptive models significantly reduced errors to CPe = 11.88, MPe = 16.02 and Toe = 5.61 mm Hg. The EHG-based IUP estimates outperformed those from traditional tocographic recordings, which had significantly higher errors (CPe = 21.93, MPe = 26.97, and TOe = 13.96). Significance: Our results show that adaptive models yield better IUP estimates than the traditional approaches and provide the best balance of the different errors computed for a better assessment of the labor progress and maternal and fetal well-being.

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 24h 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.

Multifractal analysis of uterine electromyography signals to differentiate term and preterm conditions.

In this study, an attempt has been made to identify the origin of multifractality in uterine electromyography signals and to differentiate term (gestational age > 37 weeks) and preterm (gestational age ≤ 37 weeks) conditions by multifractal detrended moving average technique. The signals obtained from a publicly available database, recorded from the abdominal surface during the second trimester, are used in this study. The signals are preprocessed and converted to shuffle and surrogate series to examine the source of multifractality. Multifractal detrended moving average algorithm is applied on all the signals. The presence of multifractality is verified using scaling exponents, and multifractal spectral features are extracted from the spectrum. The variation of multifractal features in term and preterm conditions is analyzed statistically using Student's t-test. The results of scaling exponents show that the uterine electromyography or electrohysterography signals reveal multifractal characteristics in term and preterm conditions. Further investigation indicates the existence of long-range correlation as the primary source of multifractality. Among all extracted features, strength of multifractality, exponent index, and maximum and peak singularity exponents are statistically significant ( p < 0.05) in differentiating term and preterm conditions. The coefficient of variation is found to be lower for strength of multifractality and peak singularity exponent, which reveal that these features exhibit less inter-subject variance. Hence, it appears that multifractal analysis can aid in the diagnosis of preterm or term delivery of pregnant women.