Analysis Of Fetal Electrocardiogram Research Articles

Surfao-cardio study: Trans-abdominal fetal ECG analysis in the second trimester of pregnancy in a cohort of healthy fetuses

Surfao-cardio study: Trans-abdominal fetal ECG analysis in the second trimester of pregnancy in a cohort of healthy fetuses

Fetal electrocardiogram signal extraction and analysis method combining fast independent component analysis algorithm and convolutional neural network

Fetal electrocardiogram (ECG) signals provide important clinical information for early diagnosis and intervention of fetal abnormalities. In this paper, we propose a new method for fetal ECG signal extraction and analysis. Firstly, an improved fast independent component analysis method and singular value decomposition algorithm are combined to extract high-quality fetal ECG signals and solve the waveform missing problem. Secondly, a novel convolutional neural network model is applied to identify the QRS complex waves of fetal ECG signals and effectively solve the waveform overlap problem. Finally, high quality extraction of fetal ECG signals and intelligent recognition of fetal QRS complex waves are achieved. The method proposed in this paper was validated with the data from the PhysioNet computing in cardiology challenge 2013 database of the Complex Physiological Signals Research Resource Network. The results show that the average sensitivity and positive prediction values of the extraction algorithm are 98.21% and 99.52%, respectively, and the average sensitivity and positive prediction values of the QRS complex waves recognition algorithm are 94.14% and 95.80%, respectively, which are better than those of other research results. In conclusion, the algorithm and model proposed in this paper have some practical significance and may provide a theoretical basis for clinical medical decision making in the future.

Fetal ECG extraction using temporal convolutional encoder-decoder network

To extract weak fetal ECG signals from mixed ECG signals recorded from maternal abdominal wall for accurate analysis of fetal heart rate and fetal ECG patterns. By exploiting the superior nonlinear mapping ability of deep convolutional network, we developed a nonlinear adaptive noise cancelling (nonlinear ANC) extraction framework based on a temporal convolutional encoder-decoder network for extracting fetal ECG signals. We first constructed a deep temporal convolutional network (TCED-Net) model for fetal ECG signal extraction, and with the maternal chest ECG signal as the reference signal, the maternal ECG component in the abdominal mixed signal was estimated using this model. The estimated maternal ECG component was subtracted from the mixed abdominal ECG signals to obtain the fetal ECG component. Experimental analyses were performed using synthetic ECG signals (FECGSYNDB) and clinical ECG signals (NIFECGDB, PCDB) to test the performance of the propose method. The results of experiments on the FECGSYNDB dataset showed that the proposed approach achieved good performance in F1-score (98.89%), mean-square-error (MSE; 0.20) and quality signalto-noise ratio (qSNR; 7.84). The F1- score reached 99.1% on the NIFECGDB dataset and 98.61% on the PCDB dataset. The R peak detection accuracy index of the proposed method was higher than the existing best-performing algorithms such as EKF (F1=93.84%), ES-RNN (F1=97.20%) and AECG-DecompNet (F1=95.43%) by 5.05%, 1.9% and 3.18%, respectively. The fetal ECG signals extracted using the proposed method are clearer than those by the existing algorithms, suggesting the potential value this method for effective fetal health monitoring during pregnancy.

A novel fetal ecg signal extraction from maternal ecg signal using conditional generative adversarial networks (CGAN)

Fetal Electrocardiogram (ECG) signal extraction from non-invasive abdominal ECG signal is one of the important clinical practices followed to observe the fetal health state. Information about heart growth and health conditions of a fetus can be observed from fetal ECG signals. However, acquiring fetal ECG from abdominal ECG signals is still considered as a challenging task in biomedical analysis. This is mainly due to corrupted high amplitude maternal ECG signals, low signal to noise ratio of fetal ECG signal, difficulties in reduction of QRS (Q wave, R wave, S wave) complexities, fetal ECG signal superimposed characteristics, other motion, and electromyography artifacts. To reduce these conventional challenges, in fetal ECG analysis of a novel Conditional Generative adversarial network (CGAN) is introduced in this research work to extract the fetal ECG signal. The proposed classification model was classified efficiently in fetal ECG signals from non-invasive abdominal ECG signals. The experimental analysis demonstrates that the proposed network model provides better results in terms of sensitivity, specificity, and accuracy compared to the conventional fetal ECG extraction models like singular value decomposition, periodic component analysis, and Adaptive neuro-fuzzy inference system.

STAN: a reappraisal of its clinical usefulness.

The automatic analysis of fetal ECG in labor has been introduced as an adjunct of traditional cardiotocography with the aim to improve the identification of fetuses with intrapartum hypoxia. Several randomized controlled trials and meta-analyses have produced conflicting results, with the most recent randomized controlled trial not demonstrating any improvement in either neonatal outcomes or reduction in operative birth rates. The objective of this review article is to present the state of art about the use of STAN technology in labor ward.

STAN: a reappraisal of its clinical usefulness.

The automatic analysis of fetal ECG in labor has been introduced as an adjunct of traditional cardiotocography with the aim to improve the identification of fetuses with intrapartum hypoxia. Several randomized controlled trials and meta-analyses have produced conflicting results, with the most recent randomized controlled trial not demonstrating any improvement in either neonatal outcomes or reduction in operative birth rates. The objective of this review article is to present the state of art about the use of STAN technology in labor ward.

A Novel Tool for Non-Invasive Fetal Electrocardiography Research: the NInFEA Dataset.

In this work, a novel open-source dataset for noninvasive fetal electrocardiography research is presented. It is composed of 60 high-quality electrophysiological recordings acquired between the 21st and the 27th weeks of gestation. For each acquisition, whose average duration is 30.5 s, 24 unipolar abdominal leads and three bipolar thoracic leads were included, along with a maternal respiration signal collected by a thoracic resistive belt. The chosen electrodes positioning map allows reproducing up to ten setups presented in the scientific literature. Each biopotential recording was acquired synchronously with the corresponding fetal cardiac pulsed-wave Doppler (PWD) signal, to provide complete information about the fetal cardiac cycle, both from the electrical and mechanical point of view.This is the first dataset allowing the non-invasive fetal ECG analysis even in early pregnancies with a ground truth about the fetal heart activity, given by the PWD signal. For this reason, it can be used to assess fetal ECG extraction algorithms requiring multiple channels, eventually including maternal references. This dataset is being released on Physionet by the end of June 2020 and will be continuously improved in the framework of the Non-Invasive Fetal ECG Analysis (NInFEA) project of the University of Cagliari (Italy).

ST analysis of the fetal electrocardiogram - Comments on recent experimental data.

In their paper, Andriessen at al present a validation of fetal ECG analysis and the clinical STAN device in midgestation fetal lambs exposed to 25 minutes of umbilical cord occlusion. The study presents results that contrast remarkably from previously published experimental data which raises a number of questions and comments.The most striking finding of Andriessen et al is the recording of an extremely high number of alarms from the STAN equipment during control conditions when no alarms at all are expected. These patterns have never been seen, neither in the clinical situation nor in our own fetal sheep studies. The reason for this becomes apparent when their way of recording the FECG is scrutinized. In their assessment of STAN, Andriessen at al use an assumed negative aVF lead with the assumption that it will reflect the FECG in the same way as the unipolar scalp lead used clinically. The signal used for disqualification of STAN is itself not qualified to properly represent the fetal scalp lead signal that STAN is designed for. To question a proven technology is fully accepted but those attempting would be asked to argue along fully validated data and related analysis including questioning of their own data.

Fetal QRS Detection in Noninvasive Abdominal Electrocardiograms Using Principal Component Analysis and Discrete Wavelet Transforms with Signal Quality Estimation.

Background: Fetal heart rate (FHR) extracted from abdominal electrocardiogram (ECG) is a powerful non-invasive method in appropriately assessing the fetus well-being during pregnancy. Despite significant advances in the field of electrocardiography, the analysis of fetal ECG (FECG) signal is considered a challenging issue which is mainly due to low signal to noise ratio (SNR) of FECG.Objective: In this study, we present an approach for accurately locating the fetal QRS complexes in non-invasive FECG.Materials and Methods: In this experimental study, the proposed method included 4 steps. In step 1, comb notching filter was employed to pre-process the abdominal ECG (AECG). Furthermore, low frequency noises were omitted using wavelet decomposition. In next step, principal component analysis (PCA) and signal quality assessment (SQA) were used to obtain an optimal AECG reference channel for maternal R-peaks detection. In step 3, maternal ECG (MECG) was removed from mixture signal and FECG was extracted. In final step, the extracted FECG was first decomposed by discrete wavelet transforms at level 10. Then, by employing details of levels 2, 3, 4, the new FECG signal was reconstructed in which various noises and artifacts were removed and FECG components whose frequency were close to the fetal QRS complexes remained which increased the performance of the method.Results: For evaluation, 15 recordings of PhysioNet Noninvasive FECG database were used and the average F1 measure of 98.77% was obtained. Conclusion: The results indicate that use of both an efficient analysis of major component of AECG along with a signal quality assessment technique has a promising performance in FECG analysis.

Efficient Fetal-Maternal ECG Signal Separation from Two Channel Maternal Abdominal ECG via Diffusion-Based Channel Selection.

There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery. Based on the diffusion-based channel selection, here we present the mathematical formalism and clinical validation of an algorithm capable of accurate separation of maternal and fetal ECG from a two channel signal acquired over maternal abdomen. The proposed algorithm is the first algorithm, to the best of the authors' knowledge, focusing on the fetal ECG analysis based on two channel maternal abdominal ECG signal, and we apply it to two publicly available databases, the PhysioNet non-invasive fECG database (adfecgdb) and the 2013 PhysioNet/Computing in Cardiology Challenge (CinC2013), to validate the algorithm. The state-of-the-art results are achieved when compared with other available algorithms. Particularly, the F1 score for the R peak detection achieves 99.3% for the adfecgdb and 87.93% for the CinC2013, and the mean absolute error for the estimated R peak locations is 4.53 ms for the adfecgdb and 6.21 ms for the CinC2013. The method has the potential to be applied to other fetal cardiogenic signals, including cardiac doppler signals.

Does ST analysis of fetal ECG reduce cesarean section rate for fetal distress?

Objective: Each pregravidic, gestational or intrapartum complication may, per se, have a different impact on the operative delivery rate, regardless of which fetal monitoring system was adopted. We wanted to verify if CTG plus ST interval analysis (STAN) of fetal ECG might be able to reduce the number of operative delivery for fetal distress in high-risk pregnancies compared to low-risk women monitored with CTG alone.Materials and methods: In this cohort study, we evaluated 100 high-risk pregnancies consecutively with STAN® S31 (Neoventa Medical, Gotenburg, Sweden) and 160 low-risk pregnancies consecutively with continuous CTG (Hewlett Packard, 50 IP, Palo Alto, CA).Results: We found that STAN monitoring, although associated with a higher total operativity rate, both vaginal (11% versus 3.12%, p = 0.015) and cesarean (17% versus 4.37%, p = 0.001), reduced the cesarean section rate performed for fetal distress (29.41% versus 85.71%, p = 0.023) compared with low-risk CTG monitoring group.Conclusion: CTG plus ST interval analysis of fetal ECG reduced the risk of operative cesarean delivery for fetal distress in high-risk gestations.

Separation and Analysis of Fetal-ECG Signals From Compressed Sensed Abdominal ECG Recordings.

Analysis of fetal electrocardiogram (f-ECG) waveforms as well as fetal heart-rate (fHR) evaluation provide important information about the condition of the fetus during pregnancy. A continuous monitoring of f-ECG, for example using the technologies already applied for adults ECG tele-monitor-ing (e.g., Wireless Body Sensor Networks (WBSNs)), may increase early detection of fetal arrhythmias. In this study, we propose a novel framework, based on compressive sensing (CS) theory, for the compression and joint detection/classification of mother and fetal heart beats. Our scheme is based on the sparse representation of the components derived from independent component analysis (ICA), which we propose to apply directly in the compressed domain. Detection and classification is based on the activated atoms in a specifically designed reconstruction dictionary. Validation of the proposed compression and detection framework has been done on two publicly available datasets, showing promising results (sensitivity S = 92.5 %, P += 92 % , F1 = 92.2 % for the Silesia dataset and S = 78 % , P += 77 %, F1 = 77.5 % for the Challenge dataset A, with average reconstruction quality PRD = 8.5 % and PRD = 7.5 %, respectively). The experiments confirm that the proposed framework may be used for compression of abdominal f-ECG and to obtain real-time information of the fHR, providing a suitable solution for real time, very low-power f-ECG monitoring. To the authors' knowledge, this is the first time that a framework for the low-power CS compression of fetal abdominal ECG is proposed combined with a beat detector for an fHR estimation.

1: Fetal ECG analysis of the ST segment as an adjunct to intrapartum fetal heart rate monitoring: a randomized clinical trial

1: Fetal ECG analysis of the ST segment as an adjunct to intrapartum fetal heart rate monitoring: a randomized clinical trial

An efficient unsupervised fetal QRS complex detection from abdominal maternal ECG

Non-invasive fetal heart rate is of great relevance in clinical practice to monitor fetal health state during pregnancy. To date, however, despite significant advances in the field of electrocardiography, the analysis of abdominal fetal ECG is considered a challenging problem for biomedical and signal processing communities. This is mainly due to the low signal-to-noise ratio of fetal ECG and difficulties in cancellation of maternal QRS complexes, motion and electromyographic artefacts. In this paper we present an efficient unsupervised algorithm for fetal QRS complex detection from abdominal multichannel signal recordings combining ICA and maternal ECG cancelling, which outperforms each single method. The signal is first pre-processed to remove impulsive artefacts, baseline wandering and power line interference. The following steps are then applied: maternal ECG extraction through independent component analysis (ICA); maternal QRS detection; maternal ECG cancelling through weighted singular value decomposition; enhancing of fetal ECG through ICA and fetal QRS detection. We participated in the Physionet/Computing in Cardiology Challenge 2013, obtaining the top official scores of the challenge (among 53 teams of participants) of event 1 and event 2 concerning fetal heart rate and fetal interbeat intervals estimation section. The developed algorithms are released as open-source on the Physionet website.

Efectividad de la electrocardiografía fetal frente a la pulsioximetría para la determinación del bienestar fetal

Labor poses a potential risk for fetal well-being. New methods have been developed of continuous fetal pulse oximetry and fetal electrocardiogram analysis (STAN®). Our aim was to compare clinical outcomes after the STAN® system or pulse oximetry to determine fetal well-being during labor. We carried out specific searches in the major databases and applied predefined inclusion and exclusion criteria. Two prospective studies and a systematic review were selected.The instrumental delivery rate was higher with STAN® than with pulse oximetry. Metabolic acidosis rates, neonatal encephalopathy rates, and the number of pH determinations were lower when STAN® was used in combination with cardiotocography than when cardiotocography was used alone.

A Comparison of Single Channel Fetal ECG Extraction Methods

The abdominal electrocardiogram (ECG) provides a non-invasive method for monitoring the fetal cardiac activity in pregnant women. However, the temporal and frequency overlap between the fetal ECG (FECG), the maternal ECG (MECG) and noise results in a challenging source separation problem. This work seeks to compare temporal extraction methods for extracting the fetal signal and estimating fetal heart rate. A novel method for MECG cancelation using an echo state neural network (ESN) based filtering approach was compared with the least mean square (LMS), the recursive least square (RLS) adaptive filter and template subtraction (TS) techniques. Analysis was performed using real signals from two databases composing a total of 4 h 22 min of data from nine pregnant women with 37,452 reference fetal beats. The effects of preprocessing the signals was empirically evaluated. The results demonstrate that the ESN based algorithm performs best on the test data with an F1 measure of 90.2% as compared to the LMS (87.9%), RLS (88.2%) and the TS (89.3%) techniques. Results suggest that a higher baseline wander high pass cut-off frequency than traditionally used for FECG analysis significantly increases performance for all evaluated methods. Open source code for the benchmark methods are made available to allow comparison and reproducibility on the public domain data.

What is the Gold Standard for Intrapartum Fetal Monitoring?

The health authorities of Stockholm county recently published a Health Technology Assessment report: Fetal monitoring with computerized STAN analysis during labor - a systematic review with the aim to ensure that high quality research information on costs, effectiveness and broader impact of health technologies is analysed and presented in the most efficient way for those who use, manage and work in this field. The report claims to analyse available research in relation to ST interval analysis of fetal electrocardiogram (STAN) and concludes that scientific evidence for advantages of the STAN technology for maternal and fetal outcome was insufficient and that clinical use cannot be recommended and should be restricted to research protocols. The Norwegian reference group for fetal surveillance points out that the report suffers from two insufficiencies: selection bias by not providing a complete collection of the evidence for the clinical performance of the STAN technology and, secondly, that it does not provide evidence-based alternative methods.

NInFEA: an embedded framework for the real-time evaluation of fetal ECG extraction algorithms

Fetal electrocardiogram (ECG) extraction from non-invasive biopotential recordings is a long-standing research topic. Despite the significant number of algorithms presented in the scientific literature, it is difficult to find information about embedded hardware implementations able to provide real-time support for the required features, bridging the gap between theory and practice. This article presents the NInFEA (non-invasive fetal ECG analysis) tool, an embedded hardware/software framework based on the hybrid dual-core OMAP-L137 low-power processor for the real-time evaluation of fetal ECG extraction algorithms. The hybrid platform, including a digital signal processor (DSP) and a general-purpose processor (GPP), allows achieving the best performance compared with single-core architectures. The GPP provides a portable graphical user interface, whereas the DSP is extensively used for advanced signal processing tasks. As a case study, three state-of-the-art fetal ECG extraction algorithms have been ported onto NInFEA, along with some support routines needed to provide the additional information required by the clinicians and supported by the user interface. NInFEA can be regarded both as a reference design for similar applications and as a common embedded low-power testbed for real-time fetal ECG extraction algorithms.

Swedish Randomized Controlled Trial of Cardiotocography Only Versus Cardiotocography Plus ST Analysis of Fetal Electrocardiogram Revisited

OBJECTIVE: To undertake a renewed analysis of data from the previously published Swedish randomized controlled trial on intrapartum fetal monitoring with cardiotocography (CTG-only) vs. CTG plus ST ...

What is the gold standard for intrapartum fetal monitoring?

The health authorities of Stockholm county recently published a Health Technology Assessment report: "Fetal monitoring with computerized STAN analysis during labor - a systematic review" with the aim to ensure that high quality research information on costs, effectiveness and broader impact of health technologies is analysed and presented in the most efficient way for those who use, manage and work in this field. The report claims to analyse available research in relation to ST interval analysis of fetal electrocardiogram (STAN) and concludes that scientific evidence for advantages of the STAN technology for maternal and fetal outcome was insufficient and that clinical use cannot be recommended and should be restricted to research protocols. The Norwegian reference group for fetal surveillance points out that the report suffers from two insufficiencies: selection bias by not providing a complete collection of the evidence for the clinical performance of the STAN technology and, secondly, that it does not provide evidence-based alternative methods.