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

A deep learning framework for noninvasive fetal ECG signal extraction

Introduction: The availability of proactive techniques for health monitoring is essential to reducing fetal mortality and avoiding complications in fetal wellbeing. In harsh circumstances such as pandemics, earthquakes, and low-resource settings, the incompetence of many healthcare systems worldwide in providing essential services, especially for pregnant women, is critical. Being able to continuously monitor the fetus in hospitals and homes in a direct and fast manner is very important in such conditions. Methods: Monitoring the health of the baby can potentially be accomplished through the computation of vital bio-signal measures using a clear fetal electrocardiogram (ECG) signal. The aim of this study is to develop a framework to detect and identify the R-peaks of the fetal ECG directly from a 12 channel abdominal composite signal. Thus, signals were recorded noninvasively from 70 pregnant (healthy and with health conditions) women with no records of fetal abnormalities. The proposed model employs a recurrent neural network architecture to robustly detect the fetal ECG R-peaks. Results: To test the proposed framework, we performed both subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) tests. The proposed framework achieved average accuracy values of 94.2% and 88.8%, respectively. More specifically, the leave-one-subject-out test accuracy was 86.7% during the challenging period of vernix caseosa layer formation. Furthermore, we computed the fetal heart rate from the detected R-peaks, and the demonstrated results highlight the robustness of the proposed framework. Discussion: This work has the potential to cater to the critical industry of maternal and fetal healthcare as well as advance related applications.

An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery

The existing non-invasive automated preterm birth prediction methods rely on the use of uterine electrohysterogram (EHG) records coming from spontaneous preterm and term deliveries, and are indifferent to term induced and cesarean section deliveries. In order to enhance current publicly available pool of term EHG records, we developed a new EHG dataset, Induced Cesarean EHG DataSet (ICEHG DS), containing 126 30-minute EHG records, recorded early (23rd week), and/or later (31st week) during pregnancy, of those pregnancies that were expected to end in spontaneous term delivery, but ended in induced or cesarean section delivery. The records were collected at the University Medical Center Ljubljana, Ljubljana, Slovenia. The dataset includes 38 and 43, early and later, induced; 11 and 8, early and later, cesarean; and 13 and 13, early and later, induced and cesarean EHG records. This dataset enables better understanding of the underlying physiological mechanisms involved during pregnancies ending in induced and cesarean deliveries, and provides a robust and more realistic assessment of the performance of automated preterm birth prediction methods.

Use of External Uterine Electromyography Across Stage I Labor

INTRODUCTION

Highly sensitive, external uterine electromyography (EMG) measures myometrial electrical activity and is noninvasive compared with the clinical intrauterine pressure catheter. Most experimental studies have measured EMG in 30-minute epochs, limiting the utility of this instrumentation in intrapartum clinical practice. To test proof of concept, surface uterine EMG contraction activity was continuously collected throughout the first stage of labor from healthy women at term gestation with (n = 3) and without (n = 1) epidural or combined spinal-epidural analgesia for a maximal length of 11 hours and 24 minutes.

METHODS

EMG activity was recorded concurrently with tocodynamometer (toco) signals, using a pair of electrodes on the left and right sides of the maternal umbilicus with grounds attached to both hips of the reclining woman in labor. The preamplifier cutoff frequency settings were appropriate to monitor smooth muscle contraction in labor, with the analog high-pass filter set at 0.05 Hz and the low-pass filter at 1.50 Hz. Signals were sampled at 100 Hz, transmitted to a computer, and visualized by Chart 4.2 software. EMG data from epochs at baseline, during the pre-epidural fluid bolus and at the 60-minute post-epidural test dose, and at 3, 5, 6, and 8 cm dilatation were analyzed for burst power spectrum peak frequency (Hz), burst power spectrum amplitude (mV2 ), and burst duration (seconds).

RESULTS

Uterine EMG contractile bursts were preceded and followed by a stable baseline and coincided with toco contractions. Movement artifacts were negligible, and large movement artifacts were easily distinguishable. The EMG bursts and toco contractions remained clearly identifiable, even when one woman without epidural analgesia stood beside the bed laboring for approximately 10 minutes. Burst spectral components fell within the expected 0.34-to-1.00 Hz range for term labor.

DISCUSSION

High-quality data demonstrate that EMG instrumentation effectively and accurately measures uterine contraction parameters across the first stage of term labor.

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.