fECG Signal Research Articles

A non-invasive multimodal foetal ECG\u2013Doppler dataset for antenatal cardiology research

Non-invasive foetal electrocardiography (fECG) continues to be an open topic for research. The development of standard algorithms for the extraction of the fECG from the maternal electrophysiological interference is limited by the lack of publicly available reference datasets that could be used to benchmark different algorithms while providing a ground truth for foetal heart activity when an invasive scalp lead is unavailable. In this work, we present the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA), the first open-access multimodal early-pregnancy dataset in the field that features simultaneous non-invasive electrophysiological recordings and foetal pulsed-wave Doppler (PWD). The dataset is mainly conceived for researchers working on fECG signal processing algorithms. The dataset includes 60 entries from 39 pregnant women, between the 21st and 27th week of gestation. Each dataset entry comprises 27 electrophysiological channels (2048 Hz, 22 bits), a maternal respiration signal, synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians during signal acquisition. MATLAB snippets for data processing are also provided.

Wavelet denoising as a post-processing enhancement method for non-invasive foetal electrocardiography

Background and ObjectiveThe detection of a clean and undistorted foetal electrocardiogram (fECG) from non-invasive abdominal recordings is an open research issue. Several physiological and instrumental noise sources hamper this process, even after that powerful fECG extraction algorithms have been used. Wavelet denoising is widely used for the improvement of the SNR in biomedical signal processing. This work aims to systematically assess conventional and unconventional wavelet denoising approaches for the post-processing of fECG signals by providing evidence of their effectiveness in improving fECG SNR while preserving the morphology of the signal of interest. MethodsThe stationary wavelet transform (SWT) and the stationary wavelet packet transform (SWPT) were considered, due to their different granularity in the sub-band decomposition of the signal. Three thresholds from the literature, either conventional (Minimax and Universal) and unconventional, were selected. To this aim, the unconventional one was adapted for the first time to SWPT by trying different approaches. The decomposition depth was studied in relation to the characteristics of the fECG signal. Synthetic and real datasets, publicly available for benchmarking and research, were used for quantitative analysis in terms of noise reduction, foetal QRS detection performance and preservation of fECG morphology. ResultsThe adoption of wavelet denoising approaches generally improved the SNR. Interestingly, the SWT methods outperformed the SWPT ones in morphology preservation (p<0.04) and SNR (p<0.0003), despite their coarser granularity in the sub-band analysis. Remarkably, the Han et al. threshold, adopted for the first time for fECG processing, provided the best quality improvement (p<0.003). ConclusionsThe findings of our systematic analysis suggest that particular care must be taken when selecting and using wavelet denoising for non-invasive fECG signal post-processing. In particular, despite the general noise reduction capability, signal morphology can be significantly altered on the basis of the parameterization of the wavelet methods. Remarkably, the adoption of a finer sub-band decomposition provided by the wavelet packet was not able to improve the quality of the processing.

Model based estimation of QT intervals in non-invasive fetal ECG signals.

The end timing of T waves in fetal electrocardiogram (fECG) is important for the evaluation of ST and QT intervals which are vital markers to assess cardiac repolarization patterns. Monitoring malignant fetal arrhythmias in utero is fundamental to care in congenital heart anomalies preventing perinatal death. Currently, reliable detection of end of T waves is possible only by using fetal scalp ECG (fsECG) and fetal magnetocardiography (fMCG). fMCG is expensive and less accessible and fsECG is an invasive technique available only during intrapartum period. Another safer and affordable alternative is the non-invasive fECG (nfECG) which can provide similar assessment provided by fsECG and fMECG but with less accuracy (not beat by beat). Detection of T waves using nfECG is challenging because of their low amplitudes and high noise. In this study, a novel model-based method that estimates the end of T waves in nfECG signals is proposed. The repolarization phase has been modeled as the discharging phase of a capacitor. To test the model, fECG signals were collected from 58 pregnant women (age: (34 ± 6) years old) bearing normal and abnormal fetuses with gestational age (GA) 20-41 weeks. QT and QTc intervals have been calculated to test the level of agreement between the model-based and reference values (fsECG and Doppler Ultrasound (DUS) signals) in normal subjects. The results of the test showed high agreement between model-based and reference values (difference < 5%), which implies that the proposed model could be an alternative method to detect the end of T waves in nfECG signals.

Simulator of a Full Fetal Electrocardiogram Measurement Chain by Multichannel Capacitive Sensing

Long-term ambulatory monitoring of the fetal heart rate (fHR) can greatly increase insight into fetal well-being and reduce pregnancy risks. Unfortunately, the existing solutions using wet or dry electrodes are unsuitable for long-term fHR monitoring due to the use of a gel or direct skin contact. Capacitive electrodes can measure an electrocardiographic (ECG) signal through clothes and, therefore, are perfectly suitable for long-term monitoring of the fHR. However, capacitive fetal ECG (fECG) measurements are challenging due to the high sensitivity of capacitive sensors to motion artifacts (MAs) and the low amplitude of the fECG. This article aims at investigating the feasibility of fECG measurements using capacitive electrodes with dedicated postprocessing algorithms for signal-to-noise ratio (SNR) improvement and MA reduction. To this end, a novel simulator of the full measurement chain is realized that generates multichannel capacitive fECG data with artificial MAs and system noise. A dedicated blind source separation (BSS) algorithm is then employed for MA removal and fECG extraction. The extracted fECG is evaluated by SNR calculation and R-peak detection. Our results show that the BSS algorithm may extract the fECG signal from noisy capacitive data. In addition, lower system noise or higher number of channels may lead to better fECG extraction. A maximum increase of 0.5 dB in the SNR and decrease of 80.7 % in the R-peak detection error are observed with increased electrode number from 8 to 20. Our findings provide useful insights for the hardware design of a capacitive fECG measurement system.

Composite Deep Belief Network approach for enhanced Antepartum foetal electrocardiogram signal

Antepartum Foetal surveillance is the most vital epoch of investigation during the pregnancy period. This surveillance would provide an opening to plan and manage the Foetus during Intrapartum and Antepartum stages of pregnancy. Moreover, it will help to identify high risk Foetuses during pregnancies which are complicated by maternal health conditions like diabetes mellitus, intrauterine growth restriction, etc. The foetal electrocardiogram (fECG) signal can be detected in the course of pregnancy from the Antepartum stage. Generally, fECG signal analysis is not carried out for Foetal surveillance. Rather, the traditional methodologies like phonocardiogram, etc. are being utilized. The reason is the unavailability of an effective methodology for providing good quality fECG signal. The proposal of a hybrid tactic called Bayesian Deep Belief Network (BDBN) for fECG signal enhancement is presented in this article. The proposed BDBN technique involves Baye’s filtering methodology in amalgamation with Deep Belief Network. The Baye’s filtering was employed to eliminate undesired signal components. Deep learning (DL) technique was utilized with Deep belief network (DBN) to extract high quality fECG signal. The methodology resulted with good quality fECG signal which is indeed valuable for timely Physician analysis.

Detection of End of T-wave in Fetal ECG Using Recurrence Plots.

Automatic detection of fetal ECG features can assist in diagnosis of fetal cardiac complications and may reduce the time required for diagnosis. Detection of the end of the repolarization period wave in ECG has been proven challenging due to its low amplitude and low frequency range. The prolongation of end of T-wave is associated with sudden cardiac death, thus, methods that can accurately pinpoint it is highly desirable for early diagnosis of cardiac diseases. In this paper, a technique based on recurrence plots is developed for the detection of end of T-wave. The developed technique was tested on maternal ECG (mECG), fetal scalp ECG (fsECG) and non-invasive fetal ECG (nfECG) records. The technique was able to detect end of T-waves in all of the mECG beats, 75% of the non-invasive fECG beats (verified by simultaneously captured doppler ultrasound signals) and 78% of the fsECG beats. Detection of fECG signals were more challenging than mECG signals due to the noise and their low amplitude T-waves.

HMM-based Supervised Machine Learning Framework for the Detection of fECG R-R Peak Locations

ObjectiveFetal Electro Cardiogram (fECG) provides critical information on the wellbeing of a foetus heart in its developing stages in the mother's womb. The objective of this work is to extract fECG which is buried in a composite signal consisting of itself, maternal ECG (mECG) and noises contributed from various unavoidable sources. In the past, the challenge of extracting fECG from the composite signal was dealt with by Stochastic Weiner filter, model-based Kalman filter and other adaptive filtering techniques. Blind Source Separation (BSS) based Independent Component Analysis (ICA) has shown an edge over the adaptive filtering techniques as the former does not require a reference signal. Recently, data-driven machine learning techniques e.g., adaptive neural networks, adaptive neuro-fuzzy inference system, support vector machine (SVM) are also applied. MethodThis work pursues hidden Markov model (HMM)-based supervised machine learning frame-work for the determination of the location of fECG QRS complex from the composite abdominal signal. HMM is used to model the underlying hidden states of the observable time series of the extracted and separated fECG data with its QRS peak location as one of the hidden states. The state transition probabilities are estimated in the training phase using the annotated data sets. Afterwards, using the estimated HMM networks, fQRS locations are detected in the testing phase. To evaluate the proposed technique, the accuracy of the correct detection of QRS complex with respect to the correct annotation of QRS complex location is considered and quantified by the sensitivity, probability of false alarm, and accuracy. ResultsThe best results that have been achieved using the proposed method are: accuracy – 97.1%, correct detection rate (translated to sensitivity) – 100%, and false alarm rate – 2.89%. ConclusionTwo primary challenges in these methods are finding the right reference threshold for the normalization of the extracted fECG signal during the initial trials and limitation of discrete frame work of HMM signal (converted from continuous time) which only offers a countable number of levels in observations. By feeding the posterior probabilities, obtained from SVM, into HMM, as emission probabilities, can further improve the accuracy of fQRS location detection.

A novel modular fetal ECG STAN and HRV analysis: Towards robust hypoxia detection.

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.

Extraction of foetal ECG from abdominal ECG by nonlinear transformation and estimations

Background and objectiveThis paper proposes a simple yet effective method for the extraction of foetal ECG from abdominal ECG which is necessary due to similar spatial and temporal content of mother and foetal ECG. MethodsThe proposed algorithm for extraction of foetal ECG (fECG) from abdominal signal uses single channel. Pre-processing of abdominal ECG (abdECG) has been done to eliminate noise and condition the signal. The maternal ECG R-peaks have been detected based on thresholding, first order Gaussian differentiation and zero cross detection on pre-processed signal. Having identified R-peaks and pre-processed signal as base, using Maximum Likelihood Estimation, one beat including QRS complex morphology of maternal ECG (mECG) has been constructed. Extraction of maternal ECG from abdECG is done based on the constructed beat, R-peak locations and its corresponding QRS complex of abdECG. Extracted mECG has been cancelled from abdECG. This results in foetal ECG with residual noise. The noise has been reduced by Polynomial Approximation and Total Variation (PATV) to improve SNR. This approach ensures no loss of partially or completely overlapped fECG signals due to mECG removal. The algorithm is tested on three database namely daISy (DBI), Physiobank challenge 2013 (DBII) and abdominal and direct foetal ECG database (adfecgdb) of Physiobank (DBIII). ResultsThe algorithm detected no false positives or false negatives with certain channel for DBI, DBII and DBIII which shows that the proposed algorithm can achieve good performance. Overall accuracy and sensitivity of the system is 98.53% and 100% for DBI. Best accuracy and sensitivity of 97.77% and 98.63% are obtained for DBII. Best accuracy of 92.41% and sensitivity of 93.8% are obtained for DBIII. Correlation coefficient between actual foetal heart rate (fHR) and estimated fHR of 0.66 for DBII and 0.59 for DBIII is obtained. The method has obtained overall F1 score of 99.25% for DBI, 96.04% for DBII and 94.25% for DBIII. It has obtained a best MSE of fHR and overall MSE of R-R interval which is 10.8bpm2 and 2.2 ms for DBII, 12bpm2 and 2.14 ms for DBIII. ConclusionThe results for different public databases show that the proposed method is capable of providing good results. The foetal QRS, R-peaks and R-R intervals have also been obtained in this method. Thus, it gives a significant contribution in the required area of research.

Group sparse recovery in impulsive noise via alternating direction method of multipliers

In this paper, we consider the recovery of group sparse signals corrupted by impulsive noise. In some recent literature, researchers have utilized stable data fitting models, like l 1 -norm, Huber penalty function and Lorentzian-norm, to substitute the l 2 -norm data fidelity model to obtain more robust performance. In this paper, a stable model is developed, which exploits the generalized l p -norm as the measure for the error for sparse reconstruction. In order to address this model, we propose an efficient alternative direction method of multipliers, which includes the proximity operator of l p -norm functions to the framework of Lagrangian methods. Besides, to guarantee the convergence of the algorithm in the case of 0 ≤ p < 1 (nonconvex case), we took advantage of a smoothing strategy. For both 0 ≤ p < 1 (nonconvex case) and 1 ≤ p ≤ 2 (convex case), we have derived the conditions of the convergence for the proposed algorithm. Moreover, under the block restricted isometry property with constant δ τ k 0 < τ / ( 4 − τ ) for 0 < τ < 4 / 3 and δ τ k 0 < ( τ − 1 ) / τ for τ ≥ 4 / 3 , a sharp sufficient condition for group sparse recovery in the presence of impulsive noise and its associated error upper bound estimation are established. Numerical results based on the synthetic block sparse signals and the real-world FECG signals demonstrate the effectiveness and robustness of new algorithm in highly impulsive noise.

Optimization of RLS Algorithm for Hybrid Method ICA-RLS

This paper deals with a design and implementation of a new approach to the extraction of fetal electrocardiography (fECG) signal by hybrid method combining independent component analysis (ICA) and adaptive algorithm based on recursive optimal adaptation (Recursive Least Square, RLS). The abdominal and direct fetal electrocardiogram database (ADFECGDB) was selected for the validation based on estimation of detection accuracy (ACC) relative to the reference signal recorded by means of fetal scalp electrode. The paper provides the tests of the RLS-based filter order setting in the range of 2 to 100 as well as optimization of the input channel combination to achieve the most accurate detection. In total, 11 electrode combinations were created out of 4 abdominal channels, and tested as the inputs of the ICA-RLS hybrid method. The results show that the method is very promising. The highest score was achieved for recording r0l ACC = 99.228 % with combination of input channels 1, 2, and 3 along with the RLS algorithm filter order 4.

A novel Bayesian deep learning methodology for enhanced foetal cardiac signal mining

ABSTRACTCardiac defects are amongst the most common birth defects. Cardiac diagnosis is indispensably imperative in the foetal stage as it might help provide an opportunity to plan and manage the baby during Antepartum and Intrapartum stages, when the baby is born. It is from the Antepartum stage where the foetal electrocardiogram (fECG) signal can actually be detected. At present, monitoring the foetus is completely focused on the heart rate. Currently fECG analysis is used in the clinical domain to analyse heart rate and the allied variations. Analysis using the morphology of the fECG is generally not undertaken for cardiac-anomaly populations. The ultimate reason for this scenario is due to unavailability in technology to yield trustworthy fECG measurements with desired quality required by Physicians. A novel hybrid methodology called BDL (Bayesian Deep Learning) methodology is proposed. The BDL includes a Bayesian filter and a deep learning (DL) Artificial Intelligent neural network for maternal electrocardiogram (mECG) elimination and non-linear artefacts removal to yield high quality non-invasive fECG signal. The outcomes of the research by the proposed BDL system proved valuable and provided high quality fECG signal for efficient foetal diagnosis.

Comparative Effectiveness of ICA and PCA in Extraction of Fetal ECG From Abdominal Signals: Toward Non-invasive Fetal Monitoring.

Non-adaptive signal processing methods have been successfully applied to extract fetal electrocardiograms (fECGs) from maternal abdominal electrocardiograms (aECGs); and initial tests to evaluate the efficacy of these methods have been carried out by using synthetic data. Nevertheless, performance evaluation of such methods using real data is a much more challenging task and has neither been fully undertaken nor reported in the literature. Therefore, in this investigation, we aimed to compare the effectiveness of two popular non-adaptive methods (the ICA and PCA) to explore the non-invasive (NI) extraction (separation) of fECGs, also known as NI-fECGs from aECGs. The performance of these well-known methods was enhanced by an adaptive algorithm, compensating amplitude difference and time shift between the estimated components. We used real signals compiled in 12 recordings (real01–real12). Five of the recordings were from the publicly available database (PhysioNet-Abdominal and Direct Fetal Electrocardiogram Database), which included data recorded by multiple abdominal electrodes. Seven more recordings were acquired by measurements performed at the Institute of Medical Technology and Equipment, Zabrze, Poland. Therefore, in total we used 60 min of data (i.e., around 88,000 R waves) for our experiments. This dataset covers different gestational ages, fetal positions, fetal positions, maternal body mass indices (BMI), etc. Such a unique heterogeneous dataset of sufficient length combining continuous Fetal Scalp Electrode (FSE) acquired and abdominal ECG recordings allows for robust testing of the applied ICA and PCA methods. The performance of these signal separation methods was then comprehensively evaluated by comparing the fetal Heart Rate (fHR) values determined from the extracted fECGs with those calculated from the fECG signals recorded directly by means of a reference FSE. Additionally, we tested the possibility of non-invasive ST analysis (NI-STAN) by determining the T/QRS ratio. Our results demonstrated that even though these advanced signal processing methods are suitable for the non-invasive estimation and monitoring of the fHR information from maternal aECG signals, their utility for further morphological analysis of the extracted fECG signals remains questionable and warrants further work.

Non-invasive acquisition of fetal ECG from the maternal xyphoid process: a feasibility study in pregnant sheep and a call for open data sets

Objective: The utility of fetal heart rate (FHR) monitoring can only be achieved with an acquisition sampling rate that preserves the underlying physiological information on the millisecond time scale (1000 Hz rather than 4 Hz). For such acquisition, fetal ECG (fECG) is required, rather than the ultrasound to derive FHR. We tested one recently developed algorithm, SAVER, and two widely applied algorithms to extract fECG from a single-channel maternal ECG signal recorded over the xyphoid process rather than the routine abdominal signal. Approach: At 126dG, ECG was attached to near-term ewe and fetal shoulders, manubrium and xyphoid processes (n = 12). fECG served as the ground-truth to which the fetal ECG signal extracted from the simultaneously-acquired maternal ECG was compared. All fetuses were in good health during surgery (pH 7.29 ± 0.03, pO2 33.2 ± 8.4, pCO2 56.0 ± 7.8, O2Sat 78.3 ± 7.6, lactate 2.8 ± 0.6, BE −0.3 ± 2.4). Main result: In all animals, single lead fECG extraction algorithm could not extract fECG from the maternal ECG signal over the xyphoid process with the F1 less than 50%. Significance: The applied fECG extraction algorithms might be unsuitable for the maternal ECG signal over the xyphoid process, or the latter does not contain strong enough fECG signal, although the lead is near the mother’s abdomen. Fetal sheep model is widely used to mimic various fetal conditions, yet ECG recordings in a public data set form are not available to test the predictive ability of fECG and FHR. We are making this data set openly available to other researchers to foster non-invasive fECG acquisition in this animal model.

ECG Electrode Configuration to Extract Real Time FECG Signals

Monitoring fetal health early in pregnancy helps to avert possible fetal damage and even fetal death. Among various fetal monitoring techniques the invasive fetal scalp electrode method is the most accurate method at labor. However this method may cause risk to the baby if used for antepartum monitoring. The Non-invasive fetal ECG (NIFECG) is a simple and the next best method to extract fetal heart rate (FHR) before full term. NIFECG can extract the fetal morphology from the FECG signal which can aid in diagnosing fetal health ahead of delivery. This paper aims to locate the most optimum points to place the ECG electrodes on the maternal abdomen so as to get the best FECG signals. After reviewing few previous techniques, we proposed three schemes using a standard 12 lead ECG recorder. It was observed that the scheme 2 successfully displayed the average FHR as compared to the ultrasound report of a single subject. A further researching and experimental procedures are required in this abdominal ECG (aECG) placement electrode positioning schemes before a standardized electrode configuration which can be introduced in clinical practice.

Blind Maternal-Fetal ECG Separation Based on the Time-Scale Image TSI and SVD – ICA Methods

Fetal heart monitoring yields vital information about the fetus health and can support medical decision making in critical situations. A compound signal is obtained noninvasively by placing electrodes on the abdomen area of the mother which contains maternal and fetal ECG signals contaminated by various other signals from body and externally induced noises. As a result, the basic problem is to extract the fECG signal from the mixture of mECG and fECG, where the interfering mECG is a much stronger. This problem has been addressed in several works, in this paper, we propose a novel blind-source separation method to extract fetal ECG when given only a single channel recording. Our approach is based on the wavelet theory and SVD-ICA methods. It is pointed out that the presented algorithm is validated by Matlab simulation and can be implemented on real time life by using embedded system such as DSP processor.

Non-Adaptive Methods of Fetal ECG Signal Processing

Abdominal fetal ElectroCardioGrams (fECGs) carry a wealth of information about the fetus including fetal Heart Rate (fHR) and signal morphology during different stages of pregnancy. Here we report our results on the implementation and evaluation of two non-adaptive signal processing methods suitable for fECG signal extraction, namely: the Independent Component Analysis (ICA) and the Principal Component Analysis (PCA) Methods. We used the fetal heart rate extracted from fECG signals (in Beats Per Minute - BPM) and Signal-to-Noise Ratio (SNR) as effective performance evaluation metrics for our applied methods. Our findings demonstrated that given adequate SNR, these methods produced excellent results in accurate determination of fHR. Furthermore, we found out that compared to the PCA Method, the ICA Method produces a lower variance in the detection of the fHR.

Influence of gestation age on the performance of adaptive systems for fetal ECG extraction

The main aims of this paper are to study the influence of the Gestation Age (GA) on the quality of recorded abdominal ECG (aECG) signals and to evaluate the performance of the LMS and RLS adaptive signal processing algorithms in the extraction of the fetal ECG (fECG) signal component from such signals. This influence is quantified as a function of the Signal-to-Noise Ratio (SNR). Our research shows that these adaptive algorithms with optimized settings can successfully be applied to extract fECG signals from the maternal aECG signals as early as the 30th week of GA, hence addressing a limitation (37 weeks or labor) in commercially available monitoring systems. We demonstrate that before this gestational age, the SNR of the maternal aECG signal is too low for these adaptive algorithms to work effectively and produce satisfactory results.

Use of radiotelemetry to assess perinatal cardiac function in the ovine fetus and newborn.

The late gestation fetal ECG (fECG) has traditionally been difficult to characterize due to the low fECG signal relative to high maternal noise. Although new technologies have improved the feasibility of its acquisition and separation, little is known about its development in late gestation, a period in which the fetal heart undergoes extensive maturational changes. Here, we describe a method for the chronic implantation of radiotelemetry devices into late gestation ovine fetuses to characterize parameters of the fECG following surgery, throughout late gestation, and in the perinatal period. We found no significant changes in mean aortic pressure (MAP), heart rate (HR), or ECG in the 5 days following implantation; however, HR decreased in the first 24 h following the end of surgery, with associated increases in RR, PR, and QRS intervals. Over the last 14 days of fetal life, fetal MAP significantly increased, and HR significantly decreased, as expected. MAP and HR increased as labor progressed. Although there were no significant changes over time in the ECG during late gestation, the duration of the PR interval initially decreased and then increased as birth approached. These results indicate that although critical maturational changes occur in the late gestation fetal myocardium, the mechanisms that control the cardiac conduction are relatively mature in late gestation. The study demonstrates that radiotelemetry can be successfully used to assess fetal cardiac function, in particular conduction, through the process of labor and delivery, and may therefore be a useful tool for study of peripartum cardiac events.

A novel LabVIEW-based multi-channel non-invasive abdominal maternal-fetal electrocardiogram signal generator

This paper describes the design, construction, and testing of a multi-channel fetal electrocardiogram (fECG) signal generator based on LabVIEW. Special attention is paid to the fetal heart development in relation to the fetus’ anatomy, physiology, and pathology. The non-invasive signal generator enables many parameters to be set, including fetal heart rate (FHR), maternal heart rate (MHR), gestational age (GA), fECG interferences (biological and technical artifacts), as well as other fECG signal characteristics. Furthermore, based on the change in the FHR and in the T wave-to-QRS complex ratio (T/QRS), the generator enables manifestations of hypoxic states (hypoxemia, hypoxia, and asphyxia) to be monitored while complying with clinical recommendations for classifications in cardiotocography (CTG) and fECG ST segment analysis (STAN). The generator can also produce synthetic signals with defined properties for 6 input leads (4 abdominal and 2 thoracic). Such signals are well suited to the testing of new and existing methods of fECG processing and are effective in suppressing maternal ECG while non-invasively monitoring abdominal fECG. They may also contribute to the development of a new diagnostic method, which may be referred to as non-invasive trans-abdominal CTG + STAN. The functional prototype is based on virtual instrumentation using the LabVIEW developmental environment and its associated data acquisition measurement cards (DAQmx). The generator also makes it possible to create synthetic signals and measure actual fetal and maternal ECGs by means of bioelectrodes.