An automated methodology for fetal heart rate extraction from the abdominal electrocardiogram

A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography

Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-quality NI-FECG remains a challenge. Recording waveforms of sufficient quality for clinical use typically requires human visual inspection of each recording. A Signal Quality Index (SQI) can help to automate this task but, contrary to adult ECG, work on SQIs for NI-FECG is sparse. In this paper, a multi-channel signal quality classifier for NI-FECG waveforms is presented. The model can be used during the capture of NI-FECG to assist technicians to record high-quality waveforms, which is currently a labour-intensive task. A Convolutional Neural Network (CNN) is trained to distinguish between NI-FECG segments of high and low quality. NI-FECG recordings with one maternal channel and three abdominal channels were collected from 100 subjects during a routine hospital screening (102.6 min of data). The model achieves an average 10-fold cross-validated AUC of 0.95 ± 0.02. The results show that the model can reliably assess the FECG signal quality on our dataset. The proposed model can improve the automated capture and analysis of NI-FECG as well as reduce technician labour time.

Non-Invasive Fetal Electrocardiogram Monitoring Techniques: Potential and Future Research Opportunities in Smart Textiles

During the pregnancy, fetal electrocardiogram (FECG) is deployed to analyze fetal heart rate (FHR) of the fetus to indicate the growth and health of the fetus to determine any abnormalities and prevent diseases. The fetal electrocardiogram monitoring can be carried out either invasively by placing the electrodes on the scalp of the fetus, involving the skin penetration and the risk of infection, or non-invasively by recording the fetal heart rate signal from the mother’s abdomen through a placement of electrodes deploying portable, wearable devices. Non-invasive fetal electrocardiogram (NIFECG) is an evolving technology in fetal surveillance because of the comfort to the pregnant women and being achieved remotely, specifically in the unprecedented circumstances such as pandemic or COVID-19. Textiles have been at the heart of human technological progress for thousands of years, with textile developments closely tied to key inventions that have shaped societies. The relatively recent invention of smart textiles is set to push boundaries again and has already opened the potential for garments relevant to medicine, and health monitoring. This paper aims to discuss the different technologies and methods used in non-invasive fetal electrocardiogram (NIFECG) monitoring as well as the potential and future research directions of NIFECG in the smart textiles area.

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.

A non-invasive multimodal foetal ECG-Doppler 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.

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Objective

Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes.

Materials and methods

We searched English articles on EMBASE, PubMed and SCOPUS. Search terms included ML, AI, pregnancy and informatics. We included research articles and book chapters, excluding conference papers, editorials and notes.

Results

We identified 127 distinct studies from our queries that were relevant to our topic and included in the review. We found that supervised learning methods were more popular (n = 69) than unsupervised methods (n = 9). Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). Methods such as DL are beginning to gain traction (n = 13). Common areas within the pregnancy domain where AI and ML methods were used the most include prenatal care (e.g. fetal anomalies, placental functioning) (n = 73); perinatal care, birth and delivery (n = 20); and preterm birth (n = 13). Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9).

Conclusions

Overall, we found that ML and AI methods are being employed to optimize pregnancy outcomes, including modern DL methods (n = 13). Future research should focus on less-studied pregnancy domain areas, including postnatal and postpartum care (n = 2). Also, more work on clinical adoption of AI methods and the ethical implications of such adoption is needed.

Non-invasive fetal ECG extraction using discrete wavelet transform recursive inverse adaptive algorithm

BACKGROUND

Fetal heart activity adds significant information about the status of the fetus health. Early diagnosis of issues in the heart before delivery allows early intervention and significantly improves the treatment.

OBJECTIVE

This paper presents a new adaptive filtering algorithm for fetal electrocardiogram (FECG) extraction from the maternal abdominal signal, known in literature as abdominal electrocardiogram (AECG) signal. Fetal QRS complex waves will be identified and extracted accurately for fetal health care and monitoring purposes.

METHODS

We use discrete wavelet transform recursive inverse (DWT-RI) adaptive filtering algorithm for this objective. Thoracic maternal electrocardiogram (MECG) is used as a reference in the proposed algorithm and FECG components are extracted from AECG signal after suppressing the MECG projections. The proposed algorithm is compared to other typical adaptive filtering algorithms, least mean squares (LMS), recursive least squares (RLS), and recursive inverse (RI).

RESULTS

Fetal QRS waveforms successful identification and extraction from AECG signal is evaluated objectively and visually and compared to other algorithms. We validated the proposed algorithm using both synthetic data and real clinical data.

CONCLUSIONS

The proposed algorithm is capable of extracting fetal QRS waveforms successfully from AECG and outperforms other adaptive filtering algorithms in terms of accuracy and positive predictivity.

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

Monitoring fetal heart rate (FHR) variability plays a fundamental role in fetal state assessment. Reliable FHR signal can be obtained from an invasive direct fetal electrocardiogram (FECG), but this is limited to labour. Alternative abdominal (indirect) FECG signals can be recorded during pregnancy and labour. Quality, however, is much lower and the maternal heart and uterine contractions provide sources of interference. Here, we present ten twenty-minute pregnancy signals and 12 five-minute labour signals. Abdominal FECG and reference direct FECG were recorded simultaneously during labour. Reference pregnancy signal data came from an automated detector and were corrected by clinical experts. The resulting dataset exhibits a large variety of interferences and clinically significant FHR patterns. We thus provide the scientific community with access to bioelectrical fetal heart activity signals that may enable the development of new methods for FECG signals analysis, and may ultimately advance the use and accuracy of abdominal electrocardiography methods.

Recovery of the fetal electrocardiogram for morphological analysis from two trans-abdominal channels via optimal shrinkage

OBJECTIVE

We propose a novel algorithm to recover fetal electrocardiogram (ECG) for both the fetal heart rate analysis and morphological analysis of its waveform from two or three trans-abdominal maternal ECG channels.

APPROACH

We design an algorithm based on the optimal-shrinkage under the wave-shape manifold model. For the fetal heart rate analysis, the algorithm is evaluated on publicly available database, 2013 PhyioNet/Computing in Cardiology Challenge, set A (CinC2013). For the morphological analysis, we analyze CinC2013 and another publicly available database, non-invasive fetal ECG arrhythmia database (nifeadb), and propose to simulate semi-real databases by mixing the MIT-BIH normal sinus rhythm database and MITDB arrhythmia database.

MAIN RESULTS

For the fetal R peak detection, the proposed algorithm outperforms all algorithms under comparison. For the morphological analysis, the algorithm provides an encouraging result in recovery of the fetal ECG waveform, including PR, QT and ST intervals, even when the fetus has arrhythmia, both in real and simulated databases.

SIGNIFICANCE

To the best of our knowledge, this is the first work focusing on recovering the fetal ECG for morphological analysis from two or three channels with an algorithm potentially applicable for continuous fetal electrocardiographic monitoring, which creates the potential for long term monitoring purpose.

Linear Phase Sharp Transition BPF to Detect Noninvasive Maternal and Fetal Heart Rate

Fetal heart rate (FHR) detection can be monitored using either direct fetal scalp electrode recording (invasive) or by indirect noninvasive technique. Weeks before delivery, the invasive method poses a risk factor to the fetus, while the latter provides accurate fetal ECG (FECG) information which can help diagnose fetal's well-being. Our technique employs variable order linear phase sharp transition (LPST) FIR band-pass filter which shows improved stopband attenuation at higher filter orders. The fetal frequency fiduciary edges form the band edges of the filter characterized by varying amounts of overlap of maternal ECG (MECG) spectrum. The one with the minimum maternal spectrum overlap was found to be optimum with no power line interference and maximum fetal heart beats being detected. The improved filtering is reflected in the enhancement of the performance of the fetal QRS detector (FQRS). The improvement has also occurred in fetal heart rate obtained using our algorithm which is in close agreement with the true reference (i.e., invasive fetal scalp ECG). The performance parameters of the FQRS detector such as sensitivity (Se), positive predictive value (PPV), and accuracy (F₁) were found to improve even for lower filter order. The same technique was extended to evaluate maternal QRS detector (MQRS) and found to yield satisfactory maternal heart rate (MHR) results.

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.

SEPARATION OF TWIN FETAL ECG FROM MATERNAL ECG USING EMPIRICAL MODE DECOMPOSITION TECHNIQUES

In this study, by using a combination of standard Empirical Mode Decomposition (EMD), Ensembling Empirical Mode Decomposition (EEMD), Completing Empirical Mode Decomposition (CEMD) and Principal Component Analysis (PCA), a new method was introduced to separate twin fetal heart rate (FHR) from maternal ECG. The data which were the results of modeling fetal and maternal ECG which be longed to 10 mothers with a sampling frequency of 250[Formula: see text]Hz. In this method, first R-wave of maternal ECG was determined, and then maternal QRS is removed. Further, to clarify these changes and increase resistance to environmental noises, PCA was used. In the next step, all FHRs related to twin fetuses were extracted from signals. Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was used for denoising. By using the proposed method for noise with an amplitude of over 10 dB, the FHR of the first and second (if any) fetuses were separated from maternal ECG with an accuracy of 93.3% and 91.1% respectively. The goal was to improve signal processing dimensions of fetal ECG and provides deeper insight about this issue using EEMD technique. It was tested on a twin fetus with the results suggesting its effectiveness even with increased number of fetuses with slight modifications.

A novel approach to the extraction of fetal electrocardiogram based on empirical mode decomposition and correlation analysis

Fetal heart rate monitoring is the process of checking the condition of the fetus during pregnancy and it would allow doctors and nurses to detect early signs of trouble during labor and delivery. The fetal ECG (FECG) signal is so weak and also is corrupted by other signals and noises, mainly by maternal ECG signal. It is so hard to acquire a noise-free, precise and reliable FECG using the conventional methods. In this study, a combination of empirical mode decomposition (EMD) algorithms, correlation and match filtering is used for extracting FECG from maternal abdominal ECG signals. The proposed method benefits from match filtering ability to detect fetal signal and QRS complex to detect weak QRS peaks. The combined method, has been applied successfully on different signal qualities, even for signals that their analysis was hard and complicated for other methods. This method is able to detect R-R intervals with high accuracy. It was proved that the complete ensemble empirical mode decomposition method provides a better frequency resolution of modes and also requires less iterations that leads to a considerably less computational cost than EMD and ensemble empirical mode decomposition and can reconstruct the FECG completely from the calculated modes. We believe that this method opens a new field in non-invasive maternal abdominal signal processing so the FECG signal could be extracted with high speed and accuracy.

Is Abdominal Fetal Electrocardiography an Alternative to Doppler Ultrasound for FHR Variability Evaluation?

Great expectations are connected with application of indirect fetal electrocardiography (FECG), especially for home telemonitoring of pregnancy. Evaluation of fetal heart rate (FHR) variability, when determined from FECG, uses the same criteria as for FHR signal acquired classically—through ultrasound Doppler method (US). Therefore, the equivalence of those two methods has to be confirmed, both in terms of recognizing classical FHR patterns: baseline, accelerations/decelerations (A/D), long-term variability (LTV), as well as evaluating the FHR variability with beat-to-beat accuracy—short-term variability (STV). The research material consisted of recordings collected from 60 patients in physiological and complicated pregnancy. The FHR signals of at least 30 min duration were acquired dually, using two systems for fetal and maternal monitoring, based on US and FECG methods. Recordings were retrospectively divided into normal (41) and abnormal (19) fetal outcome. The complex process of data synchronization and validation was performed. Obtained low level of the signal loss (4.5% for US and 1.8% for FECG method) enabled to perform both direct comparison of FHR signals, as well as indirect one—by using clinically relevant parameters. Direct comparison showed that there is no measurement bias between the acquisition methods, whereas the mean absolute difference, important for both visual and computer-aided signal analysis, was equal to 1.2 bpm. Such low differences do not affect the visual assessment of the FHR signal. However, in the indirect comparison the inconsistencies of several percent were noted. This mainly affects the acceleration (7.8%) and particularly deceleration (54%) patterns. In the signals acquired using the electrocardiography the obtained STV and LTV indices have shown significant overestimation by 10 and 50% respectively. It also turned out, that ability of clinical parameters to distinguish between normal and abnormal groups do not depend on the acquisition method. The obtained results prove that the abdominal FECG, considered as an alternative to the ultrasound approach, does not change the interpretation of the FHR signal, which was confirmed during both visual assessment and automated analysis.

Custom FPGA processing for real-time fetal ECG extraction and identification

Monitoring the fetal cardiac activity during pregnancy is of crucial importance for evaluating fetus health. However, there is a lack of automatic and reliable methods for Fetal ECG (FECG) monitoring that can perform this elaboration in real-time. In this paper, we present a hardware architecture, implemented on the Altera Stratix V FPGA, capable of separating the FECG from the maternal ECG and to correctly identify it. We evaluated our system using both synthetic and real tracks acquired from patients beyond the 20th pregnancy week. This work is part of a project aiming at developing a portable system for FECG continuous real-time monitoring. Its characteristics of reduced power consumption, real-time processing capability and reduced size make it suitable to be embedded in the overall system, that is the first proposed exploiting Blind Source Separation with this technology, to the best of our knowledge.

Fetal heart rate extraction from abdominal electrocardiograms through multivariate empirical mode decomposition

Assessment of fetal heart rate (FHR) and fetal heart rate variability (fHRV) reveals important information about fetal well-being, specifically in high risk pregnancies. Abdominal electrocardiogram (abdECG) recording is a non-invasive method to capture fetal electrocardiograms. In this paper, we propose a methodology to extract FHR (fetal RR time series) from the abdECG recordings using the recently introduced multivariate empirical mode decomposition (MEMD) technique. MEMD breaks a signal into a finite set of intrinsic mode functions (IMFs). First, elimination of the noisier abdECG channels, based on comparison of similar indexed IMFs that were obtained through the MEMD technique, is conducted. Thereafter, denoising of the remaining abdECG channels is performed by eliminating certain similar indexed IMFs. The unwanted mother QRS complexes are removed from these noise-free abdECG channels, and the candidate fetal R-peaks are detected through a wavelet based approach. The proposed methodology is validated using an open source real-life clinical database. The proposed technique resulted in a high value (0.983) of cross correlation between the detected and true FHR signals.

Noninvasive fetal electrocardiography: an overview of the signal electrophysiological meaning, recording procedures, and processing techniques

BACKGROUND

Noninvasive fetal electrocardiography (fECG), obtained positioning electrodes on the maternal abdomen, is important in safeguarding the life and the health of the unborn child. This study aims to provide a review of the state of the art of fECG, and includes a description of the parameters useful for fetus clinical evaluation; of the fECG recording procedures; and of the techniques to extract the fECG signal from the abdominal recordings.

METHODS

The fetus clinical status is inferred by analyzing growth parameters, supraventricular arrhythmias, ST-segment variability, and fetal-movement parameters from the fECG signal. This can be extracted from an abdominal recording obtained using one of the following two electrode-types configurations: pure-abdominal and mixed. Differently from the former, the latter also provides pure maternal ECG tracings. From a mathematical point of view, the abdominal recording is a summation of three signal components: the fECG signal (i.e., the signal of interest to be extracted), the abdominal maternal ECG (amECG), and the noise. Automatic extraction of fECG includes noise removal by abdominal signal prefiltration (0.5-45 Hz bandpass filter) and amECG cancellation.

CONCLUSIONS

Differences among methods rely on different techniques used to extract fECG. If pure abdominal electrode configurations are used, fECG is extracted directly from the abdominal recording using independent component analysis or template subtraction. Eventually, if mixed electrode configurations are used, the fECG can be extracted using the adaptive filtering fed with the maternal ECG recorded by the electrodes located in the woman thorax or shoulder.

Modeling quasi-periodic signals by a non-parametric model: application on fetal ECG extraction

Quasi-periodic signals can be modeled by their second order statistics as Gaussian process. This work presents a non-parametric method to model such signals. ECG, as a quasi-periodic signal, can also be modeled by such method which can help to extract the fetal ECG from the maternal ECG signal, using a single source abdominal channel. The prior information on the signal shape, and on the maternal and fetal RR interval, helps to better estimate the parameters while applying the Bayesian principles. The values of the parameters of the method, among which the R-peak instants, are accurately estimated using the Metropolis-Hastings algorithm. This estimation provides very precise values for the R-peaks, so that they can be located even between the existing time samples.

Influence of electrode placement on signal quality for ambulatory pregnancy monitoring

Noninvasive fetal health monitoring during pregnancy has become increasingly important in order to prevent complications, such as fetal hypoxia and preterm labor. With recent advances in signal processing technology using abdominal electrocardiogram (ECG) recordings, ambulatory fetal monitoring throughout pregnancy is now an important step closer to becoming feasible. The large number of electrodes required in current noise-robust solutions, however, leads to high power consumption and reduced patient comfort. In this paper, requirements for reliable fetal monitoring using a minimal number of electrodes are determined based on simulations and measurement results. To this end, a dipole-based model is proposed to simulate different electrode positions based on standard recordings. Results show a significant influence of bipolar lead orientation on maternal and fetal ECG measurement quality, as well as a significant influence of interelectrode distance for all signals of interest.

An application of reconfigurable technologies for non-invasive fetal heart rate extraction

This paper illustrates the use of a reconfigurable system for fetal electrocardiogram (FECG) estimation from mother's abdomen ECG measurements. The system is based on two different reconfigurable devices. Initially, a field-programmable analog array (FPAA) device implements the analog reconfigurable preprocessing for ECG signal acquisition. The signal processing chain continues onto a field-programmable gate array (FPGA) device, which contains all the communication and interfacing protocols along with specific digital signal processing blocks required for fundamental period extraction from FECG waveforms. The synergy between these devices provides the system the ability to change any necessary parameter during the acquisition process for enhancing the result. The use of a FPGA allows implementing different algorithms for FECG signal extraction, such as adaptive signal filtering. Preliminary works employ commercially available development platforms for test experiments, which suffice for the processing of real FECG signals from biomedical databases, as the presented results illustrate.

Low-complexity R-peak detection in ECG signals: a preliminary step towards ambulatory fetal monitoring

Non-invasive fetal health monitoring during pregnancy has become increasingly important. Recent advances in signal processing technology have enabled fetal monitoring during pregnancy, using abdominal ECG recordings. Ubiquitous ambulatory monitoring for continuous fetal health measurement is however still unfeasible due to the computational complexity of noise robust solutions. In this paper an ECG R-peak detection algorithm for ambulatory R-peak detection is proposed, as part of a fetal ECG detection algorithm. The proposed algorithm is optimized to reduce computational complexity, while increasing the R-peak detection quality compared to existing R-peak detection schemes. Validation of the algorithm is performed on two manually annotated datasets, the MIT/BIH Arrhythmia database and an in-house abdominal database. Both R-peak detection quality and computational complexity are compared to state-of-the-art algorithms as described in the literature. With a detection error rate of 0.22% and 0.12% on the MIT/BIH Arrhythmia and in-house databases, respectively, the quality of the proposed algorithm is comparable to the best state-of-the-art algorithms, at a reduced computational complexity.

Improving local PCA in pseudo phase space for fetal heart rate estimation from single lead abdominal ECG

This paper proposes an improved local principal component analysis (LPCA) in pseudo phase space for fetal heart rate estimation from a single lead abdominal ECG signal. The improved LPCA process can extract both the maternal ECG component and the fetal ECG component in an abdominal signal. The instantaneous fetal heart rate can then be estimated from the extracted fetal ECG waveform. Compared with the classical LPCA procedure and another single lead based fetal heart rate estimation method, our improved LPCA method has shown better robustness and efficiency in fetal heart estimation, testing with synthetic ECG signals and a real fetal ECG database from PhysioBank. For the real fetal ECG validating dataset of six long-duration recordings (obtained between the 22(nd) and 40(th) week of gestation), the average accuracy of the improved LPCA method is 84.1%.

Prototype of a wearable system for remote fetal monitoring during pregnancy

Fetal Heart Rate (FHR) monitoring gives important information about the fetus health state during pregnancy. This paper presents a new prototype for remote fetal monitoring. The device will allow to monitor FHR in a domiciliary context and to send fetal ECG traces to a hospital facility, where clinicians can interpret them. In this way the mother could receive prompt feedback about fetal wellbeing. The system is characterized by two units: (i) a wearable unit endowed with textile electrodes for abdominal ECG recordings and with a Field Programmable Gate Array (FPGA) board for fetal heart rate (FHR) extraction; (ii) a dock station for the transmission of the data through the telephone line. The system will allow to reduce costs in fetal monitoring, improving the assessment of fetal conditions. The device is actually in development state. In this paper, the most crucial aspects behind its fulfillment are discussed.

Detection and Processing Techniques of FECG Signal for Fetal Monitoring

Fetal electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. The ultimate reason for the interest in FECG signal analysis is in clinical diagnosis and biomedical applications. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies are becoming very important requirements in fetal monitoring. The purpose of this review paper is to illustrate the various methodologies and developed algorithms on FECG signal detection and analysis to provide efficient and effective ways of understanding the FECG signal and its nature for fetal monitoring. A comparative study has been carried out to show the performance and accuracy of various methods of FECG signal analysis for fetal monitoring. Finally, this paper further focused some of the hardware implementations using electrical signals for monitoring the fetal heart rate. This paper opens up a passage for researchers, physicians, and end users to advocate an excellent understanding of FECG signal and its analysis procedures for fetal heart rate monitoring system.

Detection of fetal heart rate through 3-D phase space analysis from multivariate abdominal recordings

A novel three-stage methodology for the detection of fetal heart rate (fHR) from multivariate abdominal ECG recordings is introduced. In the first stage, the maternal R-peaks and fiducial points (maternal QRS onset and offset) are detected, using band-pass filtering and phase space analysis. The maternal fiducial points are used to eliminate the maternal QRS complexes from the abdominal ECG recordings. In the second stage, two denoising procedures are applied to enhance the fetal QRS complexes. The phase space characteristics are employed to identify fetal heart beats not overlapping with the maternal QRSs, which are eliminated in the first stage. The extraction of the fHR is accomplished in the third stage, using a histogram-based technique in order to identify the location of the fetal heart beats that overlap with the maternal QRSs. The methodology is evaluated on simulated multichannel ECG signals, generated by a recently proposed model with various SNRs, and on real signals, recorded from pregnant women in various weeks during gestation. In both cases, the obtained results indicate high performance; in the simulated ECGs, the accuracy ranges from 72.78% to 98.61%, depending on the employed SNR, while in the real recordings, the average accuracy is 95.45%. The proposed methodology is advantageous since it copes with the existence of noise from various sources while it is applicable in multichannel abdominal recordings.

Fetal heart rate detection in multivariate abdominal ECG recordings using non-linear analysis

A novel three-stage methodology for the detection of fetal heart rate (fHR) from multivariate abdominal electrocardiogram (ECG) recordings is introduced. In the first stage, the maternal R-peaks and fiducial points (maternal QRS onset and offset) are detected. Maternal fiducial points are used to eliminate the maternal QRS complexes from the abdominal ECG recordings. In the second stage, two denoising procedures are applied to enhance the fetal QRS complexes. The phase space characteristics are employed to identify fetal heart beats not overlapping with the maternal QRSs which are eliminated in the first stage. The extraction of the fetal heart rate is accomplished in the third stage, using a histogram based technique in order to identify the location of the fetal heart beats which overlap with the maternal QRSs. The methodology is evaluated on simulated and real multichannel ECG signals. In both cases, the obtained results indicate high performance; in the simulated ECGs the accuracy ranges from 74.21-100%, depending on the employed SNR, while in the real recordings the average accuracy is 94.08%.

Maternal and fetal heart rate extraction from abdominal recordings using multi-scale principal components analysis

A three-stage methodology for the extraction of maternal and fetal heart rate using abdominal ECG leads, is presented. In the first stage, the maternal R-peaks and fiducial points (maternal QRS onset and offset) are detected, using multiscale principal components analysis (MSPCA) and the Smoothed Nonlinear Energy Operator (SNEO). Maternal fiducial points are used to eliminate the maternal QRS complexes from the abdominal ECG recordings. In the second stage, again MSPCA and SNEO are employed in order to detect the fetal heart beats that do not overlap with the maternal QRSs (eliminated from the first stage). The extraction of the fetal heart rate is accomplished in the last stage, using a histogram based technique in order to identify the positions of the fetal heart beats that overlap with the maternal QRSs. Real signals, recorded from different pregnant women and different weeks of gestation, are used for the evaluation of the proposed methodology and the obtained results indicate high performance (accuracy 95%).