Characterization of EHG contractions at term labor by nonlinear analysis

Analysis of fetal heart rate fluctuations in women diagnosed with preeclampsia during the latent phase of labor

Introduction

Fetal heart rate variability (fHRV) is a tool used to investigate the functioning of the fetal autonomic nervous system. Despite the significance of preeclampsia, fHRV during the latent phase of labor has not been extensively studied. This study aimed to evaluate fetal cardiac autonomic activity by using linear and nonlinear indices of fHRV analysis in women diagnosed with preeclampsia without hypertensive treatment during gestation, compared to normotensive women during the latent phase of labor.

Methods

A cross-sectional and exploratory study was conducted among pregnant women in the latent phase of labor, forming three study groups: normotensive or control (C, 38.8 ± 1.3 weeks of pregnancy, n = 22), preeclampsia with moderate features (P, 37.6 ± 1.4 weeks of pregnancy n = 10), and preeclampsia with severe features (SP, 36.9 ± 1.2 weeks of pregnancy, n = 12). None of the participants received anti-hypertensive treatment during their pregnancy. Linear and nonlinear features of beat-to-beat fHRV, including temporal, frequency, symbolic dynamics, and entropy measures, were analyzed to compare normotensive and preeclamptic groups.

Results

Significantly lower values of multiscale entropy (MSE) and short-term complexity index (Ci) were observed in the preeclamptic groups compared to the C group (p < 0.05). Additionally, higher values of SDNN (standard deviation of R-R intervals) and higher values of low-frequency power (LF) were found in the P group compared to the C group.

Conclusion

Our findings indicate that changes in the complexity of fetal heart rate fluctuations may indicate possible disruptions in the autonomic nervous system of fetuses in groups affected by undiagnosed preeclampsia during pregnancy. Reduced complexity and shifts in fetal autonomic cardiac activity could be associated with preeclampsia's pathophysiological mechanisms during the latent phase of labor.

Enhancing classification of preterm-term birth using continuous wavelet transform and entropy-based methods of electrohysterogram signals

INTRODUCTION

Despite vast research, premature birth's electrophysiological mechanisms are not fully understood. Prediction of preterm birth contributes to child survival by providing timely and skilled care to both mother and child. Electrohysterography is an affordable, noninvasive technique that has been highly sensitive in diagnosing preterm labor. This study aimed to choose the more appropriate combination of characteristics, such as electrode channel and bandwidth, as well as those linear, time-frequency, and nonlinear features of the electrohysterogram (EHG) for predicting preterm birth using classifiers.

METHODS

We analyzed two open-access datasets of 30 minutes of EHG obtained in regular checkups of women around 31 weeks of pregnancy who experienced premature labor (P) and term labor (T). The current approach filtered the raw EHGs in three relevant frequency subbands (0.3-1 Hz, 1-2 Hz, and 2-3Hz). The EHG time series were then segmented to create 120-second windows, from which individual characteristics were calculated. The linear, time-frequency, and nonlinear indices of EHG of each combination (channel-filter) were fed to different classifiers using feature selection techniques.

RESULTS

The best performance, i.e., 88.52% accuracy, 83.83% sensitivity, and 93.22% specificity, was obtained in the 2-3 Hz bands using Medium Frequency, Continuous Wavelet Transform (CWT), and entropy-based indices. Interestingly, CWT features were significantly different in all filter-channel combinations. The proposed study uses small samples of EHG signals to diagnose preterm birth accurately, showing their potential application in the clinical environment.

DISCUSSION

Our results suggest that CWT and novel entropy-based features of EHG could be suitable descriptors for analyzing and understanding the complex nature of preterm labor mechanisms.

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).

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.

Multifractal Analysis of Uterine Electromyography Signals for the Assessment of Progression of Pregnancy in Term Conditions

OBJECTIVES

The objectives of this paper are to examine the source of multifractality in uterine electromyography (EMG) signals and to study the progression of pregnancy in the term (gestation period > 37 weeks) conditions using multifractal detrending moving average (MFDMA) algorithm.

METHODS

The signals for the study, considered from an online database, are obtained from the surface of abdomen during the second (T1) and third trimester (T2). The existence of multifractality is tested using Hurst and scaling exponents. With the intention of identifying the origin of multifractality, the preprocessed signals are converted to shuffle and surrogate data. The original and the transformed signals are subjected to MFDMA to extract multifractal spectrum features, namely strength of multifractality, maximum, minimum, and peak singularity exponents.

RESULTS

The Hurst and scaling exponents extracted from the signals indicate that uterine EMG signals are multifractal in nature. Further analysis shows that the source of multifractality is mainly owing to the presence of long-range correlation, which is computed as 79.98% in T1 and 82.43% in T2 groups. Among the extracted features, the peak singularity exponent and strength of multifractality show statistical significance in identifying the progression of pregnancy. The corresponding coefficients of variation are found to be low, which show that these features have low intersubject variability.

CONCLUSION

It appears that the multifractal analysis can help in investigating the progressive changes in uterine muscle contractions during pregnancy.

Electrohysterography in the diagnosis of preterm birth: a review

Preterm birth (PTB) is one of the most common and serious complications in pregnancy. About 15 million preterm neonates are born every year, with ratios of 10-15% of total births. In industrialized countries, preterm delivery is responsible for 70% of mortality and 75% of morbidity in the neonatal period. Diagnostic means for its timely risk assessment are lacking and the underlying physiological mechanisms are unclear. Surface recording of the uterine myoelectrical activity (electrohysterogram, EHG) has emerged as a better uterine dynamics monitoring technique than traditional surface pressure recordings and provides information on the condition of uterine muscle in different obstetrical scenarios with emphasis on predicting preterm deliveries.

OBJECTIVE

A comprehensive review of the literature was performed on studies related to the use of the electrohysterogram in the PTB context.

APPROACH

This review presents and discusses the results according to the different types of parameter (temporal and spectral, non-linear and bivariate) used for EHG characterization.

MAIN RESULTS

Electrohysterogram analysis reveals that the uterine electrophysiological changes that precede spontaneous preterm labor are associated with contractions of more intensity, higher frequency content, faster and more organized propagated activity and stronger coupling of different uterine areas. Temporal, spectral, non-linear and bivariate EHG analyses therefore provide useful and complementary information. Classificatory techniques of different types and varying complexity have been developed to diagnose PTB. The information derived from these different types of EHG parameters, either individually or in combination, is able to provide more accurate predictions of PTB than current clinical methods. However, in order to extend EHG to clinical applications, the recording set-up should be simplified, be less intrusive and more robust-and signal analysis should be automated without requiring much supervision and yield physiologically interpretable results.

SIGNIFICANCE

This review provides a general background to PTB and describes how EHG can be used to better understand its underlying physiological mechanisms and improve its prediction. The findings will help future research workers to decide the most appropriate EHG features to be used in their analyses and facilitate future clinical EHG applications in order to improve PTB prediction.

Hilbert-Huang transform in detecting and analyzing the uterine contraction activities

OBJECTIVE

The diagnosis of labor is currently one of the most difficult problems encountered by obstetrical healthcare providers. A major health problem is the increase in the rate of preterm delivery, which is responsible for 75% of all deaths in newborns. In addition, preterm delivery is associated with several cognitive and health problems in later life and enormous costs for the health system. A better understanding of myometrial activities could help to reduce preterm deliveries and the costs associated with prematurity in the following years. Therefore, the objective of this study was to determine whether using the Hilbert-Huang transform (HHT) to analyze the uterine contraction data would help us gain a better insight of the myometrial activities of the human uterus during pregnancy.

MATERIAL AND METHODS

Uterine magnetomyographic (MMG) signals were recorded from pregnant patients at gestational ages of 32-38 weeks. The study was approved by the Human Research Advisory Board of the University of Arkansas for Medical Sciences (UAMS) and performed after obtaining written consent from each patient. The recording of transabdominal MMG signals was conducted with the SQUID Array for Reproductive Assessment (SARA, VSM MedTech Inc; Coquitlam, BC, Canada) system, which has 151 primary magnetic sensors allocated approximately 3 cm apart over an area of 850 cm(2). The arrangement of sensors is concave in nature and, in a similar lateral distance, spans the maternal abdomen longitudinally from the symphysis pubis to the uterine fundus. The recording times ranged from 12 to 28 min, and the sampling rate was 250 Hz. The data were down-sampled to 25 Hz to reduce the computational complexity and post-processed with a bandpass filter (0.05-1 Hz) because the uterine contraction activity is a band-limited process (0.05-1 Hz). The recordings of one intrauterine pressure catheter (IUPC) dataset and two mother-perceived contraction datasets were compared with the HHT results, and HHT's potential was explored through the development of a module and a series of experiments. The local energy and the instantaneous frequency derived from the intrinsic mode functions (IMFs) through HHT provide a full energy-frequency-time distribution of the data. Our objective was to determine whether HHT for each channel can help identify and localize contractions in the uterus. Human studies have been reviewed by the appropriate ethics committee and have therefore been performed in accordance with the ethical standards described in an appropriate version of the 1975 Declaration of Helsinki, as revised in 2000.

RESULTS

After comparing the IUPC and other mother-perceived contraction (STIM) datasets with HHT results, we were able to visually detect contraction locations in the HHT-processed uterine signals. For verification and validation purposes, when we further analyzed the delay time between two signals, the mechanical activity (i.e., IUPC) following the electrical activity (i.e., magnetic signal) was observed. In conclusion, our experimentations using the method introduced here revealed that there is a 75% correlation between the results obtained by HHT and IUPC data.

CONCLUSION

This study compared uterine contractions and changes in the intrauterine pressure with results obtained by HHT. In addition, using IUPC data as a validation guide, we showed that the HHT approach can be used for noise removal. There is a need for time-saving and non-subjective automatic contraction detection in the field of prenatal examination.