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Fetal heart rate (fHR) is an important indicator for monitoring of fetal cardiac health and development. The widely-used method based on ultrasound, however, is not continuous and often requires an expert to perform; thus, it is mostly used in clinics during checkups. The advances in wearable technology have paved the way for home assessment of fHR via the extraction of the mothers abdominal electrocardiogram (ECG) acquired by novel patches. Several methods have been developed for such; however, the computation is either too slow for real-time monitoring or too heavy to be performed in a wearable. In this work, we develop and validate the Lullaby algorithm - a novel method for fetal QRS extraction from aECG. The results showed that Lullaby is almost 7 times faster than existing methods with a better F1-score of 0.815, holding promise to transform perinatal monitoring.

Pregnancy complications significantly impact women and pose potential threats to the developing child’s health. Early identification of these complications is imperative for life-saving interventions. The manual analysis of cardiotocography (CTG) tests, a conventional practice among obstetricians, is both labor-intensive and unreliable. Consequently, the development of efficient fetal health classification models becomes crucial for optimizing medical resources and saving time.This study addresses the imperative for advanced fetal health classification through the application of machine learning (ML) techniques. The objective is to explore, develop, and analyze ML models capable of accurately classifying fetal health based on CTG data. The overarching goal is to enhance diagnostic precision and facilitate timely interventions.Utilizing a freely available cardiotocography data set, despite its relatively small size, the research acknowledges its rich characteristics. Various ML models, including Random Forests, Logistic Regression, Decision Trees, Support Vector Classifiers, Voting Classes, and K-Nearest Neighbors, are deployed on the data set. The analysis involves rigorous training and testing of these models to assess their efficacy in classifying fetal health.The study yields promising outcomes, with the implemented ML models achieving a notable accuracy level of 93%, surpassing previous methods. This underscores the effectiveness of the proposed models in elevating the precision of fetal health classification based on CTG data.The findings advocate for the integration of ML models into routine clinical practices, streamlining fetal health assessments. The study not only underscores the significance of early complication detection but also demonstrates the potential of ML in optimizing medical resource allocation and time efficiency. Further research is warranted to refine and expand ML applications in the context of fetal health assessment, promising advancements in prenatal care.

IntroductionCardiotocography, which consists in monitoring the fetal heart rate as well as uterine activity, is widely used in clinical practice to assess fetal wellbeing during labor and delivery in order to detect fetal hypoxia and intervene before permanent damage to the fetus. We present DeepCTG® 1.0, a model able to predict fetal acidosis from the cardiotocography signals.Materials and methodsDeepCTG® 1.0 is based on a logistic regression model fed with four features extracted from the last available 30 min segment of cardiotocography signals: the minimum and maximum values of the fetal heart rate baseline, and the area covered by accelerations and decelerations. Those four features have been selected among a larger set of 25 features. The model has been trained and evaluated on three datasets: the open CTU-UHB dataset, the SPaM dataset and a dataset built in hospital Beaujon (Clichy, France). Its performance has been compared with other published models and with nine obstetricians who have annotated the CTU-UHB cases. We have also evaluated the impact of two key factors on the performance of the model: the inclusion of cesareans in the datasets and the length of the cardiotocography segment used to compute the features fed to the model.ResultsThe AUC of the model is 0.74 on the CTU-UHB and Beaujon datasets, and between 0.77 and 0.87 on the SPaM dataset. It achieves a much lower false positive rate (12% vs. 25%) than the most frequent annotation among the nine obstetricians for the same sensitivity (45%). The performance of the model is slightly lower on the cesarean cases only (AUC = 0.74 vs. 0.76) and feeding the model with shorter CTG segments leads to a significant decrease in its performance (AUC = 0.68 with 10 min segments).DiscussionAlthough being relatively simple, DeepCTG® 1.0 reaches a good performance: it compares very favorably to clinical practice and performs slightly better than other published models based on similar approaches. It has the important characteristic of being interpretable, as the four features it is based on are known and understood by practitioners. The model could be improved further by integrating maternofetal clinical factors, using more advanced machine learning or deep learning approaches and having a more robust evaluation of the model based on a larger dataset with more pathological cases and covering more maternity centers.

Fetal phonocardiography (FPCG) is a non-invasive Fetal Heart Rate (FHR) monitoring technique that can detect vibrations and murmurs in heart sounds. However, acquiring fetal heart sounds from a wearable FPCG device is challenging due to noise and artefacts. This research contributes a resilient solution to overcome the conventional issues by adopting Artificial Intelligence (AI) with FPCG for automated FHR monitoring in an end-to-end manner, named (AI-FHR). Four sequential methodologies were used to ensure reliable and accurate FHR monitoring. The proposed method removes low-frequency noises and high-frequency noises by using Chebyshev II high-pass filters and Enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ECEEMDAN) in combination with Phase Shifted Maximal Overlap Discrete Wavelet Transform (PS-MODWT) filters, respectively. The denoised signals are segmented to reduce complexity, and the segmentation is performed using multi-agent deep Q-learning (MA-DQL). The segmented signal is provided to reduce the redundancies in cardiac cycles using the Artificial Hummingbird Optimization (AHBO) algorithm. The segmented and non-redundant signals are converted into 3D spectrograms using a machine learning algorithm called variational auto-encoder-general adversarial networks (VAE-GAN). The feature extraction and classification are carried out by adopting a hybrid of the bidirectional gated recurrent unit (BiGRU) and the multi-boosted capsule network (MBCapsNet). The proposed method was implemented and simulated using MATLAB R2020a and validated by adopting effective validation metrics. The results demonstrate that the proposed method performed better than the current method with accuracy (81.34%), sensitivity (72%), F1-score (83%), Energy (0.808 J), and complexity index (13.34). Like other optimization methods, AHO needs precise parameter adjustment in order to function well. Its performance may be greatly impacted by the selection of parameters, including population size, exploration rate, and learning rate. • Chebyshev filter and EC2EMDAN-PS-MODWT reduce low and high frequency noises. • MA-DRL and optimization algorithms reduce complexity during classification. • Machine learning spectrogram conversion to capture time, frequency, and spectral variations. • Hybrid deep learning algorithms can be used to reduce positive rates. [ABSTRACT FROM AUTHOR]

The article focuses on study conducted to compare intrapartum cardiotocography (CTG) in fetuses with Congenital heart defects (CHD) and normal fetuses. Topics discussed include using cardiotocography (CTG) with computer assessment of fetal heart rate for fetal monitoring during pregnancy; methodology used in study using sample collected from Department of Obstetrics and Gynecology, Medical University of Warsaw, and St. Sophie Specialist Hospital, Warsaw, Poland; and result of the study.

Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus's health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring. [ABSTRACT FROM AUTHOR]

This study focuses on non-invasive fetal electrocardiogram extraction based on a novel hybrid method, which combines the advantages of non-adaptive and adaptive approaches for non-invasive fetal electrocardiogram morphological analysis. Besides estimating fetal heart rate, which is the main parameter used in the clinical practice, this study provides non-invasive ST segment analysis on data from Abdominal and Direct Fetal Electrocardiogram Database consisting of simultaneous traditional - gold standard invasive fetal scalp electrode and non-invasive fetal electrocardiogram recorded during delivery. This innovative approach utilizing the combination of independent component analysis and recursive least squares algorithms has the potential to extract valuable information from non-invasive fetal electrocardiogram in order to identify eventual sign of fetal distress. This was a prospective observational study of non-invasive fetal electrocardiogram, using 4 abdominally sited electrodes, against the traditional fetal scalp electrode on 8 patients. In terms of fetal heart rate estimation, the accuracy was high for all 8 tested patients with average value equaled 0.20 beats per minute and average value of 1.96 standard deviation equaled 5.80 beats per minute. In 7 patients, it was possible to perform the ST segment analysis with high accuracy in determining T/QRS in comparison with the reference fetal scalp electrode signal with average values and 1.96 standard deviation equaled 0.008 and 0.031 respectively. This study thus demonstrates that ST segment analysis is feasible using non-invasive fECG using the proposed hybrid method.

During labor, uterine contractions trigger the response of the autonomic nervous system (ANS) of the fetus, producing sawtooth-like decelerations in the fetal heart rate (FHR) series. Under chronic hypoxia, ANS is known to regulate FHR differently with respect to healthy fetuses. In this study, we hypothesized that such different ANS regulation might also lead to a change in the FHR deceleration morphology. The hypothesis was tested in an animal model comprising nine normoxic and five chronically hypoxic fetuses that underwent a protocol of umbilical cord occlusions (UCOs). Deceleration morphologies in the fetal inter-beat time interval (FRR) series were modeled using a trapezoid with four parameters, i.e., baseline b, deceleration depth a, UCO response time τu and recovery time τr. Comparing normoxic and hypoxic sheep, we found a clear difference for τu (24.8±9.4 vs. 39.8±9.7 s; p < 0.05), a (268.1±109.5 vs. 373.0±46.0 ms; p < 0.1) and Δτ = τu − τr (13.2±6.9 vs. 23.9±7.5 s; p < 0.05). Therefore, the animal model supported the hypothesis that hypoxic fetuses have a longer response time τu and larger asymmetry Δτ as a response to UCOs. Assessing these morphological parameters during labor is challenging due to non-stationarity, phase desynchronization and noise. For this reason, in the second part of the study, we quantified whether acceleration capacity (AC), deceleration capacity (DC), and deceleration reserve (DR), computed through Phase-Rectified Signal Averaging (PRSA, known to be robust to noise), were correlated with the morphological parameters. DC, AC and DR were correlated with τu, τr and Δτ for a wide range of the PRSA parameter T (Pearson's correlation ρ > 0.8, p < 0.05). In conclusion, deceleration morphologies have been found to differ between normoxic and hypoxic sheep fetuses during UCOs. The same difference can be assessed through PRSA based parameters, further motivating future investigations on the translational potential of this methodology on human data.

Background: CTG remains the only non-invasive tool available to the maternity team for continuous monitoring of fetal well-being during labour. Despite widespread use and investment in staff training, difficulty with CTG interpretation continues to be identified as a problem in cases of fetal hypoxia, which often results in permanent brain injury. Given the recent advances in AI, it is hoped that its application to CTG will offer a better, less subjective and more reliable method of CTG interpretation.Objectives: This mini-review examines the literature and discusses the impediments to the success of AI application to CTG thus far. Prior randomised control trials (RCTs) of CTG decision support systems are reviewed from technical and clinical perspectives. A selection of novel engineering approaches, not yet validated in RCTs, are also reviewed. The review presents the key challenges that need to be addressed in order to develop a robust AI tool to identify fetal distress in a timely manner so that appropriate intervention can be made.Results: The decision support systems used in three RCTs were reviewed, summarising the algorithms, the outcomes of the trials and the limitations. Preliminary work suggests that the inclusion of clinical data can improve the performance of AI-assisted CTG. Combined with newer approaches to the classification of traces, this offers promise for rewarding future development.

Fetal phonocardiography (fPCG) is a non-invasive technique for detection of fetal heart sounds (fHSs), murmurs and vibrations. This acoustic recording is passive and provides an alternative low-cost method to ultrasonographic cardiotocography (CTG). Unfortunately, the fPCG signal is often disturbed by the wide range of artifacts that make it difficult to obtain significant diagnostic information from this signal. The study focuses on the filtering of an fPCG signal containing three types of noise (ambient noise, Gaussian noise, and movement artifacts of the mother and the fetus) having different amplitudes. Three advanced signal processing methods: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and adaptive wavelet transform (AWT) were tested and compared. The evaluation of the extraction was performed by determining the accuracy of S1 sounds detection and by determining the fetal heart rate (fHR). The evaluation of the effectiveness of the method was performed using signal-to-noise ratio (SNR), mean error of heart interval measurement (|ΔTi| ), and the statistical parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). Using the EMD method, ACC > 95$ % was achieved in 7 out of 12 types and levels of interference with average values of ACC = 88.73 %, SE = 91.57 %, PPV = 94.80 % and $\text {F1} = 93.12$ %. Using the EEMD method, ACC > 95$ % was achieved in 9 out of 12 types and levels of interference with average values of ACC = 97.49 %, SE = 97.89 %, PV = 99.53 % and F1 = 98.69 %. In this study, the best results were achieved using the AWT method, which provided ACC > 95 % in all 12 types and levels of interference with average values of ACC = 99.34 %, SE = 99.49 %, PPV = 99.85 % a F1 = 99.67 %.

Introduction: Considering the importance of evaluating resistance index (RI) and peak systolic velocity (PSV) of fetal middle cerebral artery (MCA) in determining fetal distress and fetal diseases such as anemia, this study was performed with aim to investigate the effect of changes in fetal heart rate (FHR) during resting and activity on RI and PSV indices of fetal MCA. Methods: This analytical cross-sectional study was performed by sampling method on 192 pregnant women with gestational age of 16-39 weeks referred to Kermanshah Imam Reza Hospital in 2016. Doppler ultrasound was performed in two fetal resting and activity states, and MCA-RI, MCA-PSV and FHR level were measured. Data analysis was performed by SPSS software (version 22) and Wilcoxon and paired t-tests. P

Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring.

In-utero progress of fetal development is normally assessed through manual measurements taken from ultrasound images, requiring relatively expensive equipment and well-trained personnel. Such monitoring is therefore unavailable in low- and middle-income countries (LMICs), where most of the perinatal mortality and morbidity exists. The work presented here attempts to identify a proxy for IUGR, which is a significant contributor to perinatal death in LMICs, by determining gestational age (GA) from data derived from simple-to-use, low-cost one-dimensional Doppler ultrasound (1D-DUS) and blood pressure devices. A total of 114 paired 1D-DUS recordings and maternal blood pressure recordings were selected, based on previously described signal quality measures. The average length of 1D-DUS recording was 10.43 ± 1.41 min. The min/median/max systolic and diastolic maternal blood pressures were 79/102/121 and 50.5/63.5/78.5 mmHg, respectively. GA was estimated using features derived from the 1D-DUS and maternal blood pressure using a support vector regression (SVR) approach and GA based on the last menstrual period as a reference target. A total of 50 trials of 5-fold cross-validation were performed for feature selection. The final SVR model was retrained on the training data and then tested on a held-out set comprising 28 normal weight and 25 low birth weight (LBW) newborns. The mean absolute GA error with respect to the last menstrual period was found to be 0.72 and 1.01 months for the normal and LBW newborns, respectively. The mean error in the GA estimate was shown to be negatively correlated with the birth weight. Thus, if the estimated GA is lower than the (remembered) GA calculated from last menstruation, then this could be interpreted as a potential sign of IUGR associated with LBW, and referral and intervention may be necessary. The assessment system may, therefore, have an immediate impact if coupled with suitable intervention, such as nutritional supplementation. However, a prospective clinical trial is required to show the efficacy of such a metric in the detection of IUGR and the impact of the intervention.

The visual analysis of cardiotocographic examinations is a very subjective process. The accurate detection and segmentation of the fetal heart rate (FHR) features and their correlation with the uterine contractions in time allow a better diagnostic and the possibility of anticipation of many problems related to fetal distress. This paper presents a computerized diagnostic aid system based on digital signal processing techniques to detect and segment changes in the FHR and the uterine tone signals automatically. After a pre-processing phase, the FHR baseline detection is calculated. An auxiliary signal called detection line is proposed to support the detection and segmentation processes. Then, the Hilbert transform is used with an adaptive threshold for identifying fiducial points on the fetal and maternal signals. For an antepartum (before labor) database, the positive predictivity value (PPV) is 96.80% for the FHR decelerations, and 96.18% for the FHR accelerations. For an intrapartum (during labor) database, the PPV found was 91.31% for the uterine contractions, 94.01% for the FHR decelerations, and 100% for the FHR accelerations. For the whole set of exams, PPV and SE were both 100% for the identification of FHR DIP II and prolonged decelerations.

The nonlinear analysis of biological time series provides new possibilities to improve computer aided diagnostic systems, traditionally based on linear techniques. The cardiotocography (CTG) examination records simultaneously the fetal heart rate (FHR) and the maternal uterine contractions. This paper shows, at first, that both signals present nonlinear components based on the surrogate data analysis technique and exploratory data analysis with the return (lag) plot. After that, a nonlinear complexity analysis is proposed considering two databases, intrapartum (CTG-I) and antepartum (CTG-A) with previously identified normal and suspicious/pathological groups. Approximate Entropy (ApEn) and Sample Entropy (SampEn), which are signal complexity measures, are calculated. The results show that low entropy values are found when the whole examination is considered, ApEn = 0.3244 ± 0.1078 and SampEn = 0.2351 ± 0.0758 ( average ± standard deviation). Besides, no significant difference was found between the normal ( ApEn = 0.3366 ± 0.1250 and SampEn = 0.2532 ± 0.0818 ) and suspicious/pathological ( ApEn = 0.3420 ± 0.1220 and SampEn = 0.2457 ± 0.0850 ) groups for the CTG-A database. For a better analysis, this work proposes a windowed entropy calculation considering 5-min window. The windowed entropies presented higher average values ( ApEn = 0.6505 ± 0.2301 and SampEn = 0.5290 ± 0.1188 ) for the CTG-A and ( ApEn = 0.5611 ± 0.1970 and SampEn = 0.4909 ± 0.1782 ) for the CTG-I. The changes during specific long-term events show that entropy can be considered as a first-level indicator for strong FHR decelerations ( ApEn = 0.1487 ± 0.0341 and SampEn = 0.1289 ± 0.0301 ), FHR accelerations ( ApEn = 0.1830 ± 0.1078 and SampEn = 0.1501 ± 0.0703 ) and also for pathological behavior such as sinusoidal FHR ( ApEn = 0.1808 ± 0.0445 and SampEn = 0.1621 ± 0.0381 ). [ABSTRACT FROM AUTHOR]

Fetal well-being is commonly assessed by monitoring the fetal heart rate (fHR). In clinical practice, the de facto standard technology for fHR monitoring is based on the Doppler ultrasound (US). Continuous monitoring of the fHR before and during labor is performed using a US transducer fixed on the maternal abdomen. The continuous fHR monitoring, together with simultaneous monitoring of the uterine activity, is referred to as cardiotocography (CTG). In contrast, for intermittent measurements of the fHR, a handheld Doppler US transducer is typically used. In this article, the technology of Doppler US for continuous fHR monitoring and intermittent fHR measurements is described, with emphasis on fHR monitoring for CTG. Special attention is dedicated to the measurement environment, which includes the clinical setting in which fHR monitoring is commonly performed. In addition, to understand the signal content of acquired Doppler US signals, the anatomy and physiology of the fetal heart and the surrounding maternal abdomen are described. The challenges encountered in these measurements have led to different technological strategies, which are presented and critically discussed, with a focus on the US transducer geometry, Doppler signal processing, and fHR extraction methods. [ABSTRACT FROM AUTHOR]

Introduction: The intrapartum cardiotocography (CTG) classification system by FIGO in 2015 (FIGO2015) was introduced to simplify CTG interpretation, but it is not harmonized with the fetal ECG ST analysis (STAN) algorithm from 2007 (STAN2007), which is based on the FIGO CTG system from 1987. The study aimed to determine time courses and sensitivity between the systems in classifying CTG + ST events to indicate metabolic acidosis at birth.Material and methods: Forty-four cases with umbilical cord artery metabolic acidosis were retrieved from a European multicenter database. CTG patterns and timing of the first occurring significant ST events were evaluated post hoc in consensus by an expert panel and sensitivity statistics were performed. Wilcoxon's matched-pairs signed-ranks test and McNemar's test were used with a two-tailed p < .05 regarded significant.Results: STAN2007 had a higher sensitivity (73 versus 43%, p = .0002) and alarmed for metabolic acidosis in mean 34 min earlier than the FIGO2015 system did (p = .002). In every fourth case, the time difference was ≥20 min.Conclusions: In this simulation study, surveillance with STAN2007 combined with fetal ECG ST analysis had a significantly higher sensitivity and would have alarmed for metabolic acidosis significantly earlier than the new FIGO system would have. [ABSTRACT FROM AUTHOR]

Background: Fetal electrocardiography is a developing field that provides valuable information on the fetal health during pregnancy. By early diagnosis and treatment of fetal heart problems, more survival chance is given to the infant. Objective: Here, we extract fetal ECG from maternal abdominal recordings and detect R-peaks in order to recognize fetal heart rate. On the next step, we find a better and more qualified extracted fetal ECG by using a novel approach. Materials and Methods: In this paper, a PCA/ICA-based algorithm is proposed for extracting fetal ECG, and fetal R-peaks are detected as well. The method validates the quality of extracted ECGs and selects the best candidate fetal ECG to provide the required morphological ECG features such as fetal heart rate and RR interval for more clinical examinations. The method was evaluated using the dataset which was provided by PhysioNet/Computing in Cardiology Challenge 2013. The dataset consists of 75 recordings of 4-channel ECGs each containing 1-minute length for training and 100 similar recordings for testing. Results: When the proposed algorithm was applied to the test set, the scores of 85.853 bpm2 for fetal heart rate and an error of 9.725 ms RMS for fetal RR-interval estimation were obtained. Conclusion: The results obtained with the mentioned algorithm shows the robustness of the research, and it is suggested to be used in practical fetal ECG monitoring systems.

Fetal heart rate (fHR) monitoring using the Doppler ultrasound (US) is a standard clinical practice for assessing fetal well-being before and during labor. For continuous fHR measurements, the US transducer is positioned on the maternal abdomen using a flexible belt. Due to fetal movement, the relative fetal heart location (fHL) with respect to the US transducer can change, leading to frequent periods of signal loss hampering the clinical assessment of fetal health. Consequently, the clinical staff has to repeatedly reposition the US transducer—a cumbersome task affecting clinical workflow. We propose a method to estimate the fHL during fHR monitoring to support clinicians in efficiently repositioning the US transducer. Unlike typical US transducers, which do not provide any information on the spatial fHL, we exploit the fact that multiple transducer elements are present in the array aperture of the US transducer. We developed a novel model that relates the measured Doppler power in the individual transducer elements to the fHL and use it within the probabilistic framework of an extended Kalman filter (EKF). The performance of the EKF algorithm was evaluated in simulations and in in vitro experiments using a dedicated setup of a beating fetal heart. Both simulations and in vitro experiments showed that the fHL can be determined with an accuracy of 4 mm. Furthermore, we demonstrate that when the fetal heart is drifting out of the US beam, the EKF algorithm accurately estimates the fHL up to a radial distance of 34 mm. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive fetal Electrocardiogram (fECG) Signal Extraction and Analysis Virtual Instrument that integrates various methods for detecting the R-R Intervals (RRIs) as a means to determine the fetal Heart Rate (fHR) and therefore facilitates fetal Heart Rate Variability (HRV) signal analysis. Moreover, it offers the capability to perform advanced morphological fECG signal analysis called ST segment Analysis (STAN) as it seamlessly allows the determination of the T-wave to QRS complex ratio (also called T/QRS) in the fECG signal. The integration of these signal processing and analytical modules could help clinical researchers and practitioners to noninvasively monitor and detect the life threatening hypoxic conditions that may arise in different stages of pregnancy and more importantly during delivery and could therefore lead to the reduction of unnecessary C-sections. In our experiments we used real recordings from a Fetal Scalp Electrode (FSE) as well as maternal abdominal electrodes. This Virtual Instrument (Toolbox) not only serves as a desirable platform for comparing various fECG extraction signal processing methods, it also provides an effective means to perform STAN and HRV signal analysis based on proven ECG morphological as well as Autonomic Nervous System (ANS) indices to detect hypoxic conditions. [ABSTRACT FROM AUTHOR]

Here we present the evaluation results of our novel noninvasive phonocardiographic-based fiber-optic sensor for fetal Heart Rate (fHR) detection using adaptive filtering and the NLMS Algorithm. The sensor uses two interferometric probes encapsulated inside a PolyDiMethylSiloxane (PDMS) polymer. Based on real data acquired from pregnant women in a suitable research laboratory environment, once they had given their written informed consents, we created a simplified dynamic signal model of the distribution of maternal and fetal heart sounds inside the maternal body. Building upon this signal model, we verified the functionality of our novel fiber-optic sensor and its associated adaptive filtering system using the NLMS Algorithm. The main reason why we chose this technology to develop our system was that it allows monitoring the fHR without exposing the fetus to any external energies or radiation (in contrast to the ultrasound-based Cardiotocography Method). We used objective criteria such as: Signal to Noise Ratios: SNR_in, SNR_out and Percentage Root-mean-square Difference (PRD) for our evaluations.

• We develop a clinically interpretable dual-stream AI model for the automatic diagnosis of fetal distress. • We combine clinical knowledge traction to quantize numerous clinical diagnostic indicators of authoritative guidelines. • We employ the TreeSHAP-based white box interpreter and CNN-based case tracking model to enhance clinical interpretability. • Our approach achieves a high precision pathological diagnosis of 0.963, which provides solutions for future AI-aided diagnosis systems. Clinically, Fetal Heart Rate (FHR)-based intelligent cardiotocography classification to diagnose fetal well-being is of utmost importance for obstetricians and gynecologists. However, the current Fetal Distress Diagnosis (FDD) algorithms based on Artificial Intelligence (AI) methods are designed as black box models that usually result in poor performance on clinical interpretation and lack explicit diagnostic evidence. This paper focuses on two aspects: enhancing the accuracy of FD and providing explanations for the model's decisions. Specifically, we propose a novel clinically interpretable dual-stream AI architecture for FDD. The two streams embed multi-feature category representations and the original FHR into the Digital Twin model (DT) and the deep convolutional neural Networks-based Case Tracking model (CTNet), respectively, thus marking as DT-CTNet. And the former makes the input-outcome dependency transparent, while the latter provides the samples that are the most similar to the object being diagnosed. By this design, not only is high-precision FDD achievable, but a transparent explanation with global, local and instantiation interpretation can be obtained. To the best of our knowledge, this work is the first to provide an interpretable diagnosis of fetal distress. The proposed method is comprehensively evaluated from five aspects: classification model accuracy, explanation of the classification model decision, consistency of the evidence explanation, pathological difference in visual analysis, and a comparison with state-of-the-art methods. Experimental results, which are performed on a challenging dataset, show that the proposed fetal distress diagnosis method outperforms the state-of-the-art methods and has a high accuracy (0.963). [ABSTRACT FROM AUTHOR]

Cardiotocography (CTG) is a standard tool for the assessment of fetal well-being during pregnancy and delivery. However, its interpretation is associated with high inter- and intra-observer variability. Since its introduction there have been numerous attempts to develop computerized systems assisting the evaluation of the CTG recording. Nevertheless these systems are still hardly used in a delivery ward. Two main approaches to computerized evaluation are encountered in the literature; the first one emulates existing guidelines, while the second one is more of a data-driven approach using signal processing and computational methods. The latter employs preprocessing, feature extraction/selection and a classifier that discriminates between two or more classes/conditions. These classes are often formed using the umbilical cord artery pH value measured after delivery. In this work an approach to Fetal Heart Rate (FHR) classification using pH is presented that could serve as a benchmark for reporting results on the unique open-access CTU-UHB CTG database, the largest and the only freely available database of this kind. The overall results using a very small number of features and a Least Squares Support Vector Machine (LS-SVM) classifier, are in accordance to the ones encountered in the literature and outperform the results of a baseline classification scheme proving the utility of using advanced data processing methods. Therefore the achieved results can be used as a benchmark for future research involving more informative features and/or better classification algorithms. [ABSTRACT FROM AUTHOR]

Here we present the evaluation results of our novel noninvasive phonocardiographic-based fiberoptic sensor for fetal Heart Rate (fHR) detection using adaptive filtering and the NLMS Algorithm. The sensor uses two interferometric probes encapsulated inside a PolyDiMethylSiloxane (PDMS) polymer. Based on real data acquired from pregnant women in a suitable research laboratory environment, once they had given their written informed consents, we created a simplified dynamic signal model of the distribution of maternal and fetal heart sounds inside the maternal body. Building upon this signal model, we verified the functionality of our novel fiber-optic sensor and its associated adaptive filtering system using the NLMS Algorithm. The main reason why we chose this technology to develop our system was that it allows monitoring the fHR without exposing the fetus to any external energies or radiation (in contrast to the ultrasound-based Cardiotocography Method). We used objective criteria such as: Signal to Noise Ratios: SNRin, SNRout and Percentage Root-mean-square Difference (PRD) for our evaluations. [ABSTRACT FROM AUTHOR]

This paper focuses on the design, realization, and verification of a novel phonocardiographic-based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio--SNR, Root Mean Square Error--RMSE, Sensitivity--S+, and Positive Predictive Value--PPV. [ABSTRACT FROM AUTHOR]

Background: Fetal electrocardiography is a developing field that provides valuable information on the fetal health during pregnancy. By early diagnosis and treatment of fetal heart problems, more survival chance is given to the infant. Objective: Here, we extract fetal ECG from maternal abdominal recordings and detect R-peaks in order to recognize fetal heart rate. On the next step, we find a better and more qualified extracted fetal ECG by using a novel approach. Materials and Methods: In this paper, a PCA/ICA-based algorithm is proposed for extracting fetal ECG, and fetal R-peaks are detected as well. The method validates the quality of extracted ECGs and selects the best candidate fetal ECG to provide the required morphological ECG features such as fetal heart rate and RR interval for more clinical examinations. The method was evaluated using the dataset which was provided by PhysioNet/Computing in Cardiology Challenge 2013. The dataset consists of 75 recordings of 4-channel ECGs each containing 1-minute length for training and 100 similar recordings for testing. Results: When the proposed algorithm was applied to the test set, the scores of 85.853 bpm2 for fetal heart rate and an error of 9.725 ms RMS for fetal RR-interval estimation were obtained. Conclusion: The results obtained with the mentioned algorithm shows the robustness of the research, and it is suggested to be used in practical fetal ECG monitoring systems. [ABSTRACT FROM AUTHOR]

This letter presents an efficient algorithm to detect fetal heart rates based on linear template subtraction (TS) for portable fetal heart rate monitors. After pre-processing, the covariance function windowing is used to eliminate maternal P-waves, maternal T-waves, baseline wander, and to enhance maternal R-waves and fetal R-waves. Next, fetal ECG is extracted by the modified mECG cancellation method which forms a mECG template by projecting an average maternal R-wave as a basis into exponential domains. Finally, fetal R-peaks are detected using a 2-D phase space matrix. The results show that the true positive rate, false discovery rate, and false negative rate of the proposed method are 86.80%, 5.78%, 13.20% respectively, which are better than those of other single-channel fetal ECG template extraction methods. It turns out that the modified template subtraction method has three main advantages. First, the covariance function windowing easily removes baseline wander, maternal P waves and maternal T waves. Second, the modified mECG cancellation model can extract overlapping signals without causing large waveform distortions. Last, both covariance function windowing and the modified mECG cancellation model are efficient and can operate in real time. [ABSTRACT FROM AUTHOR]

The aim of the present study is to obtain a highly objective automatic fetal heart rate (FHR) diagnosis. The neural network software was composed of three layers with the back propagation, to which 8 FHR data, including sinusoidal FHR, were input and the system was educated by the data of 20 cases with a known outcome. The output was the probability of a normal, intermediate, or pathologic outcome. The neural index studied prolonged monitoring. The neonatal states and the FHR score strongly correlated with the outcome probability. The neural index diagnosis was correct. The completed software was transferred to other computers, where the system function was correct.

Background: Fetal electrocardiography (FECG) places electrodes on the maternal abdomen to convert the fetal electrocardiosignals into fetal heart rate (FHR), improving the accuracy and comfort of pregnant woman. At the same time, FECG simplifies the procedure of long term monitoring in the perinatal period.Objective: Investigating the influence of gestational age and time of day on FHR features to distinguish between non-stress test (NST) normal fetuses and NST suspicious fetuses.Methods: A novel method of FHR baseline estimation was presented; then baseline value and fetal heart rate variation (FHRV) were analyzed in the time domain using FHR signals recorded from 52 fetuses.Results: Baseline values in 1:00, 2:00, 4:00, 5:00 and heart rate variation (HRV) distribution showed a significant difference (p< 0.05) between NST normal fetuses and NST suspicious fetuses.Conclusions: The results suggest that NST normal and suspicious fetuses had same outcome and different FHR features. Accurately distinguishing normal fetuses and suspicious fetuses is important for lowering the false positive rate and reducing unnecessary clinical intervention. [ABSTRACT FROM AUTHOR]

The purpose of this study is to provide a new method for detecting fetal QRS complexes from non-invasive fetal electrocardiogram (fECG) signal. Despite most of the current fECG processing methods which are based on separation of fECG from maternal ECG (mECG), in this study, fetal heart rate (FHR) can be extracted with high accuracy without separation of fECG from mECG. Furthermore, in this new approach thoracic channels are not necessary. These two aspects have reduced the required computational operations. Consequently, the proposed approach can be efficiently applied to different real-time healthcare and medical devices. In this work, a new method is presented for selecting the best channel which carries strongest fECG. Each channel is scored based on two criteria of noise distribution and good fetal heartbeat visibility. Another important aspect of this study is the simultaneous and combinatorial use of available fECG channels via the priority given by their scores. A combination of geometric features and wavelet-based techniques was adopted to extract FHR. Based on fetal geometric features, fECG signals were divided into three categories, and different strategies were employed to analyze each category. The method was validated using three datasets including Noninvasive fetal ECG database, DaISy and PhysioNet/Computing in Cardiology Challenge 2013. Finally, the obtained results were compared with other studies. The adopted strategies such as multi-resolution analysis, not separating fECG and mECG, intelligent channels scoring and using them simultaneously are the factors that caused the promising performance of the method. [ABSTRACT FROM AUTHOR]

Aim: To examine how complex and irregular fetal heart rate (FHR) dynamics differ between fetuses of normal pregnancies and those of pregnancies complicated by maternal anemia (MA), and to place this in the context of high-risk pregnancies. Methods: Our study population consisted of 97 pregnant women affected by MA, 118 affected by pregnancy-induced hypertension (PIH), 88 affected by gestational diabetes mellitus (GDM), 53 with preterm premature rupture of membranes (pPROM), and 356 normal pregnancies as controls. We calculated approximate entropy (ApEn), sample entropy (SampEn), and correlation dimension (CD) to quantify irregularity and the chaotic dynamics of each FHR time series. Results: The ApEn in the fetuses of the MA and PIH groups was significantly lower than that of the normal controls (P<0.05). The SampEn was significantly lower in the high-risk groups, except for the pPROM group, than in the normal controls (P<0.05). The CD in the PIH and severe MA groups was significantly lower than that of the normal controls (P<0.05, respectively). In the MA group, the dynamic indices showed a highly significant positive correlation with hemoglobin (Hb) levels (P<0.0001). Conclusion: The decreased complexity and/or irregularity in the FHR from pregnancies with MA may reflect abnormalities in the complex, integrated cardiovascular control. The irregularity and complexity of the FHR increased together with Hb levels in pregnancies with MA. Our data suggest that the integrity of the nervous system in the fetuses compromised by severe MA might result directly in adverse outcomes. [ABSTRACT FROM AUTHOR]

• The system is made of a 25-channel 2-D transducer array configuration with a two degree-of-freedom mechanical scanning system. • The intensities of Doppler signals are used as a proportional control parameter to control the steering angle of a transducer array. • The fetal heart location tracking system is capable of tracking the location of a heart and obtaining a continuous heart rate record. • The proposed system can obtain stable fetal heart rate recordings without manually relocating the probe. Doppler-detected fetal heart rate (FHR) tracking is the primary testing method for assessing fetal well-being during antepartum care. However, owing to fetal movements or maternal position variations, the transducer on the abdomen can be displaced, changing the fetal heart location (FHL) and resulting in frequent signal losses and uninterpretable results. In this study, we present FHR monitoring system for continuously maintaining FHR measurements without specifying the location of the Doppler ultrasound transducer. An algorithm is developed to track the FHL based on the intensity of the received Doppler signal. The performance is evaluated by conducting underwater experiments using an object with repeated movements. The tracking error is 7.93° on the X-axis and 1.42° on the Y-axis. Even with those tracking errors, the FHR is continuously recorded. This system can be used to obtain heart rate records without manipulating the position of the transducer and losing the signal. [ABSTRACT FROM AUTHOR]