Detection rate of fetal distress using contraction-dependent fetal heart rate variability analysis

A wearable system to track vital signs communicated from the mother to the fetus

BACKGROUND

The monitoring of fetal heart rate (FHR) before intrapartum has been crucial in modern obstetrics. FHR has been used for about 300 years to determine fetal status, leading to the development of monitoring devices to prevent fetal death during gestation. While medical devices like fetal electrocardiograms exist for disease detection, their size and cost limit individual use.

OBJECTIVE

To address cardiovascular issues during pregnancy, a mobile system is developed to display heart rates and blood pressure on mobile devices. The system is generated from a medical device with Bluetooth communication, supplementing traditional monitoring.

METHOD

The study focuses on creating a mobile system that connects to mobile operating systems, enhancing treatment, diagnosis, and patient monitoring. The mobile system displays cardiovascular data obtained from the medical device.

RESULTS

The results are expected to have an immediate impact on cases where abnormal measurement parameters of the monitoring system occur during pregnancy. The use of mobile systems or applications on smartphones is seen as beneficial in distributing processing and census of embedded health systems.

CONCLUSION

The study highlights the potential benefits of mobile systems in distributing processing for health systems, particularly in addressing cardiovascular problems during pregnancy. The creation of a mobile system for displaying cardiovascular data could significantly improve monitoring and early detection.

Uterine activity modifies the response of the fetal autonomic nervous system at preterm active labor

Background

The autonomic nervous system of preterm fetuses has a different level of maturity than term fetuses. Thus, their autonomic response to transient hypoxemia caused by uterine contractions in labor may differ. This study aims to compare the behavior of the fetal autonomic response to uterine contractions between preterm and term active labor using a novel time-frequency analysis of fetal heart rate variability (FHRV).

Methods

We performed a case-control study using fetal R-R and uterine activity time series obtained by abdominal electrical recordings from 18 women in active preterm labor (32-36 weeks of gestation) and 19 in active term labor (39-40 weeks of gestation). We analyzed 20 minutes of the fetal R-R time series by applying a Continuous Wavelet Transform (CWT) to obtain frequency (HF, 0.2-1 Hz; LF, 0.05-0.2 Hz) and time-frequency (Flux0, Flux90, and Flux45) domain features. Time domain FHRV features (SDNN, RMSSD, meanNN) were also calculated. In addition, ultra-short FHRV analysis was performed by segmenting the fetal R-R time series according to episodes of the uterine contraction and quiescent periods.

Results

No significant differences between preterm and term labor were found for FHRV features when calculated over 20 minutes. However, we found significant differences when segmenting between uterine contraction and quiescent periods. In the preterm group, the LF, Flux0, and Flux45 were higher during the average contraction episode compared with the average quiescent period (p<0.01), while in term fetuses, vagally mediated FHRV features (HF and RMSSD) were higher during the average contraction episode (p<0.05). The meanNN was lower during the strongest contraction in preterm fetuses compared to their consecutive quiescent period (p=0.008).

Conclusion

The average autonomic response to contractions in preterm fetuses shows sympathetic predominance, while term fetuses respond through parasympathetic activity. Comparison between groups during the strongest contraction showed a diminished fetal autonomic response in the preterm group. Thus, separating contraction and quiescent periods during labor allows for identifying differences in the autonomic nervous system cardiac regulation between preterm and term fetuses.

Cardiotocography analysis by empirical dynamic modeling and Gaussian processes

Introduction: During labor, fetal heart rate (FHR) and uterine activity (UA) can be continuously monitored using Cardiotocography (CTG). This is the most widely adopted approach for electronic fetal monitoring in hospitals. Both FHR and UA recordings are evaluated by obstetricians for assessing fetal well-being. Due to the complex and noisy nature of these recordings, the evaluation by obstetricians suffers from high interobserver and intraobserver variability. Machine learning is a field that has seen unprecedented advances in the past two decades and many efforts have been made in computerized analysis of CTG using machine learning methods. However, in the literature, the focus is often only on FHR signals unlike in evaluations performed by obstetricians where the UA signals are also taken into account. Methods: Machine learning is a field that has seen unprecedented advances in the past two decades and many efforts have been made in computerized analysis of CTG using machine learning methods. However, in the literature, the focus is often only on FHR signals unlike in evaluations performed by obstetricians where the UA signals are also taken into account. In this paper, we propose to model intrapartum CTG recordings from a dynamical system perspective using empirical dynamic modeling with Gaussian processes, which is a Bayesian nonparametric approach for estimation of functions. Results and Discussion: In the context of our paper, Gaussian processes are capable for simultaneous estimation of the dimensionality of attractor manifolds and reconstructing of attractor manifolds from time series data. This capacity of Gaussian processes allows for revealing causal relationships between the studied time series. Experimental results on real CTG recordings show that FHR and UA signals are causally related. More importantly, this causal relationship and estimated attractor manifolds can be exploited for several important applications in computerized analysis of CTG recordings including estimating missing FHR samples, recovering burst errors in FHR tracings and characterizing the interactions between FHR and UA signals.

Saleh A, Md Tahir N, Najim HT, Taher M. Intrapartum cardiotocography trace pattern pre-processing, features extraction and fetal health condition diagnoses based on RCOG guideline

CONTEXT

The computerization of both fetal heart rate (FHR) and intelligent classification modeling of the cardiotocograph (CTG) is one of the approaches that are utilized in assisting obstetricians in conducting initial interpretation based on (CTG) analysis. CTG tracing interpretation is crucial for the monitoring of the fetal status during weeks into the pregnancy and childbirth. Most contemporary studies rely on computer-assisted fetal heart rate (FHR) feature extraction and CTG categorization to determine the best precise diagnosis for tracking fetal health during pregnancy. Furthermore, through the utilization of a computer-assisted fetal monitoring system, the FHR patterns can be precisely detected and categorized.

OBJECTIVE

The goal of this project is to create a reliable feature extraction algorithm for the FHR as well as a systematic and viable classifier for the CTG through the utilization of the MATLAB platform, all the while adhering to the recognized Royal College of Obstetricians and Gynecologists (RCOG) recommendations.

METHOD

The compiled CTG data from spiky artifacts were cleaned by a specifically created application and compensated for missing data using the guidelines provided by RCOG and the MATLAB toolbox after the implemented data has been processed and the FHR fundamental features have been extracted, for example, the baseline, acceleration, deceleration, and baseline variability. This is followed by the classification phase based on the MATLAB environment. Next, using the guideline provided by the RCOG, the signals patterns of CTG were classified into three categories specifically as normal, abnormal (suspicious), or pathological. Furthermore, to ensure the effectiveness of the created computerized procedure and confirm the robustness of the method, the visual interpretation performed by five obstetricians is compared with the results utilizing the computerized version for the 150 CTG signals.

RESULTS

The attained CTG signal categorization results revealed that there is variability, particularly a trivial dissimilarity of approximately (+/-4 and 6) beats per minute (b.p.m.). It was demonstrated that obstetricians' observations coincide with algorithms based on deceleration type and number, except for acceleration values that differ by up to (+/-4).

DISCUSSION

The results obtained based on CTG interpretation showed that the utilization of the computerized approach employed in infirmaries and home care services for pregnant women is indeed suitable.

CONCLUSIONS

The classification based on CTG that was used for the interpretation of the FHR attribute as discussed in this study is based on the RCOG guidelines. The system is evaluated and validated by experts based on their expert opinions and was compared with the CTG feature extraction and classification algorithms developed using MATLAB.

UNSUPERVISED CLUSTERING AND ANALYSIS OF CONTRACTION-DEPENDENT FETAL HEART RATE SEGMENTS

The computer-aided interpretation of fetal heart rate (FHR) and uterine contraction (UC) has not been developed well enough for wide use in delivery rooms. The main challenges still lie in the lack of unclear and nonstandard labels for cardiotocography (CTG) recordings, and the timely prediction of fetal state during monitoring. Rather than traditional supervised approaches to FHR classification, this paper demonstrates a way to understand the UC-dependent FHR responses in an unsupervised manner. In this work, we provide a complete method for FHR-UC segment clustering and analysis via the Gaussian process latent variable model, and density-based spatial clustering. We map the UC-dependent FHR segments into a space with a visual dimension and propose a trajectory-based FHR interpretation method. Three metrics of FHR trajectory are defined and an open-access CTG database is used for testing the proposed method.

On designing a biosignal-based fetal state assessment system: A systematic mapping study

BACKGROUND AND OBJECTIVE

The patterns present in biosignals, such as fetal heart rate (FHR), are valuable indicators of fetal well-being. In designing biosignal analysis systems, the variety of approaches and technology usage impairs the decision-making for the fundamental units of the systems. There is a need for an updated overview of studies encompassing the biosignal-based fetal state assessment systems. Therefore, we propose a systematic mapping study to identify and synthesize recent research regarding the building blocks that compose these systems.

METHODS

We followed well-established guidelines to perform a systematic mapping of studies regarding the building-blocks that compose the fetal state assessment systems and published between January 2016 and January 2021. A search string was determined based on the mapping questions and the PI (population and intervention) divisions. The search string was applied in digital libraries covering the fields of computer science, engineering, and medical informatics. Then, we applied the forward snowballing technique to complement the resulting set. This process resulted in 75 primary studies selected from a total of 871 papers.

RESULTS

Selected studies were classified according to the publication types, systems design stages, datasets, and predictive capabilities. The results revealed that (i) The majority of the selected studies refer to the method as a type of publication and there is a lack of validation studies; (ii) The CTU-UHB was the most frequent biosignal-based dataset and UCI-CTG was the most frequent feature-based data; (iii) The selected studies made use of the system design stages alone or in a mixed-mode.

CONCLUSION

The results indicated that the well-established classification models achieved competitive results compared with the state-of-the-art methods in data-constrained system designs. Moreover, we identified the need for validation studies in the clinical environment.

The Impact of Different Degrees of Intraventricular Hemorrhage on Mortality and Neurological Outcomes in Very Preterm Infants: A Prospective Cohort Study

Objective

Intraventricular hemorrhage (IVH) is a common complication in preterm infants and is related to neurodevelopmental outcomes. Infants with severe IVH are at higher risk of adverse neurological outcomes and death, but the effect of low-grade IVH remains controversial. The purpose of this study was to evaluate the impact of different degrees of IVH on mortality and neurodevelopmental outcomes in very preterm infants.

Methods

Preterm infants with a gestational age of &lt;30 weeks admitted to neonatal intensive care units were included. Cerebral ultrasound was examined repeatedly until discharge or death. All infants were followed up to 18–24 months of corrected age. The impact of different grades of IVH on death and neurodevelopmental disability was assessed by multiple logistic regression.

Results

A total of 1,079 preterm infants were included, and 380 (35.2%) infants had grade I-II IVH, 74 (6.9%) infants had grade III-IV IVH, and 625 (57.9%) infants did not have IVH. The mortality in the non-IVH, I-II IVH, and III-IV IVH groups was 20.1, 19.7, and 55.2%, respectively (p &lt; 0.05), and the incidence of neurodevelopmental disabilities was 13.9, 16.1, and 43.3%, respectively (p &lt; 0.05), at 18–24 months of corrected age. After adjusting for confounding factors, preterm infants with III-IV IVH had higher rates of cerebral palsy [26.7 vs. 2.4%, OR = 6.10, 95% CI (1.840–20.231), p = 0.003], disability [43.3 vs. 13.9%, OR = 2.49, 95% CI (1.059–5.873), p = 0.037], death [55.2 vs. 20.1%, OR = 3.84, 95% CI (2.090–7.067), p &lt; 0.001], and disability + death [73.7 vs. 28.7%, OR = 4.77, 95% CI (2.518–9.021), p &lt; 0.001] compared to those without IVH. However, the mortality and the incidence of neurodevelopmental disability in infants with I-II IVH were similar to those without IVH (p &gt; 0.05).

Conclusions

Severe IVH but not mild IVH increased the risk of mortality and neurodevelopmental disability in very preterm infants.

Non-invasive fetal electrocardiography, electrohysterography and speckle-tracking echocardiography in the second trimester: study protocol of a longitudinal prospective cohort study (BEATS-study)

Background

Worldwide, hypertensive disorders of pregnancy (HDP), fetal growth restriction (FGR) and preterm birth remain the leading causes of maternal and fetal pregnancy-related mortality and (long-term) morbidity. Fetal cardiac deformation changes can be the first sign of placental dysfunction, which is associated with HDP, FGR and preterm birth. In addition, preterm birth is likely associated with changes in electrical activity across the uterine muscle. Therefore, fetal cardiac function and uterine activity can be used for the early detection of these complications in pregnancy. Fetal cardiac function and uterine activity can be assessed by two-dimensional speckle-tracking echocardiography (2D-STE), non-invasive fetal electrocardiography (NI-fECG), and electrohysterography (EHG). This study aims to generate reference values for 2D-STE, NI-fECG and EHG parameters during the second trimester of pregnancy and to investigate the diagnostic potential of these parameters in the early detection of HDP, FGR and preterm birth.

Methods

In this longitudinal prospective cohort study, eligible women will be recruited from a tertiary care hospital and a primary midwifery practice. In total, 594 initially healthy pregnant women with an uncomplicated singleton pregnancy will be included. Recordings of NI-fECG and EHG will be made weekly from 22 until 28 weeks of gestation and 2D-STE measurements will be performed 4-weekly at 16, 20, 24 and 28 weeks gestational age. Retrospectively, pregnancies complicated with pregnancy-related diseases will be excluded from the cohort. Reference values for 2D-STE, NI-fECG and EHG parameters will be assessed in uncomplicated pregnancies. After, 2D-STE, NI-fCG and EHG parameters measured during gestation in complicated pregnancies will be compared with these reference values.

Discussion

This will be the a large prospective study investigating new technologies that could potentially have a high impact on antepartum fetal monitoring.

Trial registration

Registered on 26 March 2020 in the Dutch Trial Register (NL8769) via https://www.trialregister.nl/trials and registered on 21 October 2020 to the Central Committee on Research Involving Human Subjects (NL73607.015.20) via https://www.toetsingonline.nl/to/ccmo_search.nsf/Searchform?OpenForm.

Ultrasound Doppler fetal heart rate detection algorithm analyzes the correlation between twin selective fetal growth restriction and cord blood SFass fasL level

Objective

The paper uses ultrasound Doppler fetal heart rate detection algorithm to explore the placental characteristics of monochorionic twin pregnancy with selective fetal growth restriction, and discuss the correlation between selective fetal growth restriction and cord blood SFass FasL level.

Methods

From June 1, 2019 to June 1, 2020 in our hospital, 23 cases of selective fetal growth restriction and 32 cases of uncomplicated cases were included in the monochorionic twin pregnancies whose pregnancy was terminated in our hospital (control group) research. Perfusion was completed within 24 hours after delivery of the placenta. The umbilical arteries and veins of the two fetuses were respectively perfused with four different colors of pigments. The type of anastomoses was judged according to the color of the blood vessels on the placenta surface.

Results

The selective fetal growth restriction group was higher than the control group. In the selective fetal growth restriction group and the control group, the number of anastomoses of the placental superficial arterial artery, arterial vein and venous vein were 1.0 and 1.0, 3.0 and 2.0, 0.0 and 0.0, respectively; the placental superficial arterial artery, arterial vein and venous vein. The total diameters of the anastomosed blood vessels were 2.7 and 2.2, 4.0 and 3.4, 0.0 and 0.0 mm, respectively; the total number of superficial placental anastomosed blood vessels in the selective fetal growth restriction group and the control group were 3.5 and 3.5, respectively.The total diameters were 6.9 and 6.9, respectively 5.9mm.

Conclusion

Uneven placental share and non-central attachment of the umbilical cord may be risk factors for selective fetal growth restriction in monochorionic twin pregnancy.

Doppler Ultrasound Imaging Combined with Fetal Heart Detection in Predicting Fetal Distress in Pregnancy-Induced Hypertension under the Guidance of Artificial Intelligence Algorithm

This study was to improve the feasibility and economic benefits of intelligent medical system Doppler ultrasound (DUS) imaging technology combined with fetal heart detection to predict the fetal distress in pregnancy-induced hypertension (PIH), so as to reduce the risk of deterioration of the patient's condition. The characteristics of DUS images were analyzed, and a diffusion filter reducing the specificity was adopted to improve the smooth speckle noise of DUS images. 120 pregnant women in hospital were the subjects of the study, all of whom received ultrasound cord blood flow testing and fetal heart monitoring. 88 PIH patients with fetal distress were diagnosed and included in the observation group, and 32 healthy pregnant women tested during the same period were identified as the control group. Clinical data were reviewed and analyzed. The diagnostic rates of fetal distress by simple fetal heart monitoring and DUS detection combined with fetal heart monitoring were compared. The results showed that 26.7% of fetal distress were diagnosed by fetal heart monitoring alone, and 73.3% of fetal distress were diagnosed by combined testing, so the diagnostic accuracy of the combined detection method was greatly higher than the single fetal heart detection (P < 0.05). The Pulsatility index (PI), resistance index (RI), and S/D values detected by the umbilical artery in the observation group were 1.48, 0.85, and 4.31, respectively. The PI, RI, and S/D values detected by the umbilical artery in the control group were 0.96, 0.64, and 3.59, respectively. The results of arterial detection were significantly higher than those of the control group, and the difference was of significant scientific significance (P < 0.05). In summary, the PI and RI values of the middle cerebral artery (MCA) detected by DUS diagnosis can effectively reflect the current status of the fetus in the uterus and reduce the mortality of the fetus. The images guided by DUS imaging technology can clearly show the current status of the fetus in the uterus, effectively improve the medical diagnostic efficiency, and have important reference value for the development of intelligent medical equipment.

IDENTIFICATION OF UTERINE CONTRACTIONS BY AN ENSEMBLE OF GAUSSIAN PROCESSES

Identifying uterine contractions with the aid of machine learning methods is necessary vis-á-vis their use in combination with fetal heart rates and other clinical data for the assessment of a fetus wellbeing. In this paper, we study contraction identification by processing noisy signals due to uterine activities. We propose a complete four-step method where we address the imbalanced classification problem with an ensemble Gaussian process classifier, where the Gaussian process latent variable model is used as a decision-maker. The results of both simulation and real data show promising performance compared to existing methods.

A systematic review of automated pre-processing, feature extraction and classification of cardiotocography

CONTEXT

The interpretations of cardiotocography (CTG) tracings are indeed vital to monitor fetal well-being both during pregnancy and childbirth. Currently, many studies are focusing on feature extraction and CTG classification using computer vision approach in determining the most accurate diagnosis as well as monitoring the fetal well-being during pregnancy. Additionally, a fetal monitoring system would be able to perform detection and precise quantification of fetal heart rate patterns.

OBJECTIVE

This study aimed to perform a systematic review to describe the achievements made by the researchers, summarizing findings that have been found by previous researchers in feature extraction and CTG classification, to determine criteria and evaluation methods to the taxonomies of the proposed literature in the CTG field and to distinguish aspects from relevant research in the field of CTG.

METHODS

Article search was done systematically using three databases: IEEE Xplore digital library, Science Direct, and Web of Science over a period of 5 years. The literature in the medical sciences and engineering was included in the search selection to provide a broader understanding for researchers.

RESULTS

After screening 372 articles, and based on our protocol of exclusion and inclusion criteria, for the final set of articles, 50 articles were obtained. The research literature taxonomy was divided into four stages. The first stage discussed the proposed method which presented steps and algorithms in the pre-processing stage, feature extraction and classification as well as their use in CTG (20/50 papers). The second stage included the development of a system specifically on automatic feature extraction and CTG classification (7/50 papers). The third stage consisted of reviews and survey articles on automatic feature extraction and CTG classification (3/50 papers). The last stage discussed evaluation and comparative studies to determine the best method for extracting and classifying features with comparisons based on a set of criteria (20/50 articles).

DISCUSSION

This study focused more on literature compared to techniques or methods. Also, this study conducts research and identification of various types of datasets used in surveys from publicly available, private, and commercial datasets. To analyze the results, researchers evaluated independent datasets using different techniques.

CONCLUSIONS

This systematic review contributes to understand and have insight into the relevant research in the field of CTG by surveying and classifying pertinent research efforts. This review will help to address the current research opportunities, problems and challenges, motivations, recommendations related to feature extraction and CTG classification, as well as the measurement of various performance and various data sets used by other researchers.

Does continuous cardiotocography during labor cause excessive fetal distress diagnosis and unnecessary cesarean sections?

OBJECTIVE

We aimed to evaluate the perinatal outcomes of patients who were continuously monitored by cardiotocography (CTG) during the labor and experienced cesarean operation with a diagnosis of fetal distress.

MATERIAL AND METHODS

This is a retrospective study in which records of the patients, who were diagnosed of fetal distress at Umraniye Training and Research Hospital, Department of Obstetrics and Gynecology, Istanbul, Turkey, between January 2015 and October 2020 were collated. The statistical analysis was done using the Statistical Packagefor Social Sciences version 22 software (SPSS Inc., Chicago IL, USA).

RESULTS

Of the 32,338 deliveries in this study period, 13,077 (40.4%) deliveries were through caesarean section. A total of 1504 (11.5%) of the 13,077 caesarean sections were due to fetal distress within the study period. A total of 1301 (86.5%) babies were born with ≥7 Apgar score at the1st min of delivery. NICU admission rate was 11.2% and perinatal mortality was 0.1%. More so, in the low-risk pregnancy group, the rate of the babies were born with ≥7 Apgar score at the1st min of delivery was 93.7% and NICU admission rate 2.1% and no perinatal mortality was seen. In the patient group in which pregestational and gestational diseases complicating pregnancy were excluded, newborns with meconium-stained amniotic fluid had statistically significantly lower 1st and 5th-min Apgar scores compared to the group without meconium and higher NICU admission (p = .000, p = .004 and p = .000, respectively).

CONCLUSION

The diagnosis of fetal distress should not be made only with fetal heart rate changes in CTG because this causes excessive fetal distress diagnosis and many unnecessary cesarean operations. We believe that rate of cesarean sections will decrease to the desired levels with the routine use of a method such as CTG which is easy to apply, but more sensitive and specific in the diagnosis of fetal distress.

The Noninvasive Fetal Electrocardiogram During Labor: A Review of the Literature

Importance

The introduction of the cardiotocogram (CTG) during labor has not been found to improve neonatal outcome. The search for a more reliable, less invasive, and patient-friendly technique is ongoing. The noninvasive fetal electrocardiogram (NI-fECG) has been proposed as one such alternative.

Objectives

The aim of this study was to review the literature on the performance of NI-fECG for fetal monitoring during labor. Following the PRISMA guidelines, a systematic search in MEDLINE, EMBASE, and Cochrane Library was performed. Studies involving original research investigating the performance of NI-fECG during labor were included. Animal studies and articles in languages other than English, Dutch, or German were excluded. The QUADAS-2 checklist was used for quality assessment. A descriptive analysis of the results is provided.

Results

Eight articles were included. Pooled analysis of the results of the separate studies was not possible due to heterogeneity. All studies demonstrate that it is possible to apply NI-fECG during labor. Compared with Doppler ultrasound, NI-fECG performs equal or better in most studies.

Conclusions and Relevance

NI-fECG for fetal monitoring is a promising noninvasive and patient-friendly technique that provides accurate information. Future studies should focus on signal quality throughout labor, with the aim to further optimize technical development of NI-fECG.

Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before they occur. While specific treatments are possible, they are often time-consuming, invasive, or painful, with detrimental effects for the development of the infant. In the last 40 years, heart rate variability (HRV) has emerged as a non-invasive measurement to monitor newborns and infants, but it still is underused. Hence, the present paper aims to review the utility of HRV in neonatology and the instruments available to assess it, showing how HRV could be an innovative tool in the years to come. When continuously monitored, HRV could help assess the baby’s overall wellbeing and neurological development to detect stress-/pain-related behaviors or pathological conditions, such as respiratory distress syndrome and hyperbilirubinemia, to address when to perform procedures to reduce the baby’s stress/pain and interventions, such as therapeutic hypothermia, and to avoid severe complications, such as sepsis and necrotizing enterocolitis, thus reducing mortality. Based on literature and previous experiences, the first step to efficiently introduce HRV in the NICUs could consist in a monitoring system that uses photoplethysmography, which is low-cost and non-invasive, and displays one or a few metrics with good clinical utility. However, to fully harness HRV clinical potential and to greatly improve neonatal care, the monitoring systems will have to rely on modern bioinformatics (machine learning and artificial intelligence algorithms), which could easily integrate infant’s HRV metrics, vital signs, and especially past history, thus elaborating models capable to efficiently monitor and predict the infant’s clinical conditions. For this reason, hospitals and institutions will have to establish tight collaborations between the obstetric, neonatal, and pediatric departments: this way, healthcare would truly improve in every stage of the perinatal period (from conception to the first years of life), since information about patients’ health would flow freely among different professionals, and high-quality research could be performed integrating the data recorded in those departments.

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

Fetal monitoring is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction signals obtained with a cardiotocograph (CTG). Unfortunately, CTG analysis is difficult, and the interpretation problems are mainly associated with the analysis of FHR decelerations. From that perspective, several approaches have been proposed to improve its analysis; however, the results obtained are not satisfactory enough for their implementation in clinical practice. Current clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms. In previous works, we have shown that the complete ensemble empirical mode decomposition with adaptive noise, in combination with time-varying autoregressive modeling, may be useful for the analysis of those characteristics. In this work, based on this methodology, we propose to analyze the FHR deceleration episodes separately. The main hypothesis is that the proposed feature extraction strategy applied separately to the complete signal, deceleration episodes, and resting periods (between contractions), improves the CTG classification performance compared with the analysis of only the complete signal. Results reveal that by considering the complete signal, the classification performance achieved 81.7% quality. Then, including information extracted from resting periods, it improved to 83.2%.

Relative accuracy of computerized intrapartum fetal heart rate pattern recognition by ultrasound and abdominal electrocardiogram detection

INTRODUCTION

Noninvasive fetal heart rate monitoring using transabdominal fetal electrocardiographic detection is now commercially available and has been demonstrated to be an effective alternative to traditional Doppler ultrasonographic techniques. Our objective in this study was to compare the results of computerized identification of fetal heart rate patterns generated by ultrasound-based and transabdominal fetal electrocardiogram-based techniques with simultaneously obtained fetal scalp electrode-derived heart rate information.

MATERIAL AND METHODS

We applied an objective computer-based analysis for recognition of fetal heart rate patterns (Monica Decision Support) to data obtained simultaneously from a direct fetal scalp electrode, Doppler ultrasound, and the abdominal-fetal electrocardiogram techniques. This allowed us to compare over 145 hours of fetal heart rate patterns generated by the external devices with those derived from the scalp electrode in 30 term singleton uncomplicated pregnancies during labor. The direct fetal scalp electrode is considered to be the most accurate and reliable technique used in current clinical practice, and was, therefore, used as the standard for comparison. The program quantified the baseline heart rate, long- and short-term variability. It indicated when an acceleration or deceleration was present and whether it was large or small.

RESULTS

Ultrasound was associated with significantly greater deviations from the fetal scalp electrode results than the abdominal fetal electrocardiogram technique in recognizing the correct baseline heart rate, its variability, and the presence of small and large accelerations and small decelerations. For large decelerations the two external methods were each not significantly different from the scalp electrode results.

CONCLUSIONS

Noninvasive fetal heart rate monitoring using maternal abdominal wall electrodes to detect fetal cardiac activity more reliably reproduced the computerized analysis of heart rate patterns derived from a direct fetal scalp electrode than did traditional ultrasound-based monitoring. Abdominal-fetal electrocardiogram should, therefore, be considered a primary option for externally monitored patients.

Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK)

The second Signal Processing and Monitoring in Labor workshop gathered researchers who utilize promising new research strategies and initiatives to tackle the challenges of intrapartum fetal monitoring. The workshop included a series of lectures and discussions focusing on: new algorithms and techniques for cardiotocogoraphy (CTG) and electrocardiogram acquisition and analyses; the results of a CTG evaluation challenge comparing state-of-the-art computerized methods and visual interpretation for the detection of arterial cord pH <7.05 at birth; the lack of consensus about the role of intrapartum acidemia in the etiology of fetal brain injury; the differences between methods for CTG analysis "mimicking" expert clinicians and those derived from "data-driven" analyses; a critical review of the results from two randomized controlled trials testing the former in clinical practice; and relevant insights from modern physiology-based studies. We concluded that the automated algorithms performed comparably to each other and to clinical assessment of the CTG. However, the sensitivity and specificity urgently need to be improved (both computerized and visual assessment). Data-driven CTG evaluation requires further work with large multicenter datasets based on well-defined labor outcomes. And before first tests in the clinic, there are important lessons to be learnt from clinical trials that tested automated algorithms mimicking expert CTG interpretation. In addition, transabdominal fetal electrocardiogram monitoring provides reliable CTG traces and variability estimates; and fetal electrocardiogram waveform analysis is subject to promising new research. There is a clear need for close collaboration between computing and clinical experts. We believe that progress will be possible with multidisciplinary collaborative research.