A comparison of single channel fetal ECG extraction methods

Elicitation of fetal ECG from abdominal recordings using Blind Source Separation techniques and Robust Set Membership Affine Projection algorithm for signal quality enhancement

BACKGROUND

The utilization of non-invasive techniques for fetal cardiac health surveillance is pivotal in evaluating fetal well-being throughout the gestational period. This process requires clean and interpretable fetal Electrocardiogram (fECG) signals.

METHOD

The proposed work is the novel framework for the elicitation of fECG signals from abdominal ECG (aECG) recordings of the pregnant mother. The comprehensive approach encompasses pre-processing of the raw ECG signal, Blind Source Separation techniques (BSS), Decomposition techniques like Empirical Mode Decomposition (EMD), and its variants like Ensemble Empirical Mode Decomposition (EEMD), and Complete Ensemble Empirical Mode Decomposition with Additive Noise (CEEMDAN). The Robust Set Membership Affine Projection (RSMAP) Algorithm is deployed for the enhancement of the obtained fECG signal.

RESULT

The results show significant improvements in the elicited fECG signal with a maximum Signal Noise Ratio (SNR) of 31.72 dB and correlation coefficient = 0.899, Maximum Heart Rate(MHR) obtained in the range of 108-142 bpm for all the records of abdominal ECG signals. The statistical test gave a p-value of 0.21 accepting the null hypothesis. The Abdominal and Direct Fetal Electrocardiogram Database (ABDFECGDB) from PhysioNet has been used for this analysis.

CONCLUSION

The proposed framework demonstrates a robust and effective method for the elicitation and enhancement of fECG signals from the abdominal recordings.

Fetal QRS extraction from single-channel abdominal ECG using adaptive improved permutation entropy

Fetal electrocardiogram (fECG) monitoring is crucial for assessing fetal condition during pregnancy. However, current fECG extraction algorithms are not suitable for wearable devices due to their high computational cost and multi-channel signal requirement. The paper introduces a novel and efficient algorithm called Adaptive Improved Permutation Entropy (AIPE), which can extract fetal QRS from a single-channel abdominal ECG (aECG). The proposed algorithm is robust and computationally efficient, making it a reliable and effective solution for wearable devices. To evaluate the performance of the proposed algorithm, we utilized our clinical data obtained from a pilot study with 10 subjects, each recording lasting 20 min. Additionally, data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations were simulated. The proposed methodology demonstrates an average positive predictive value ( + P ) of 91.0227%, sensitivity (Se) of 90.4726%, and F1 score of 90.6525% from the PhysioNet 2013 Challenge bank, outperforming other methods. The results suggest that AIPE could enable continuous home-based monitoring of unborn babies, even when mothers are not engaging in any hard physical activities.

A Nonlinear Functional Link Multilayer Perceptron Using Volterra Series as an Adaptive Noise Canceler for the Extraction of Fetal Electrocardiogram

Uninterrupted monitoring of fetal cardiac health is essential for the timely diagnosis of congenital diseases. The maternal Electrocardiogram (mECG), which has the most significant impact, always tampers with the signals collected from the pregnant woman's abdomen. So, an efficient nonlinear filtering network based on artificial neural network (ANN) is required to eliminate the maternal part from the abdominal Electrocardiogram (aECG) that is traveled from the thoracic of the mother to the abdomen following nonlinear dynamics. In this work, we have presented an adaptive noise canceler (ANC) using 3-layer perceptron architecture where the inputs are expanded by the functional link expansion using the second-order Volterra series, and the weights are updated using backpropagation. The adaptive filter approximates the nonlinear mapping between the thoracic Electrocardiogram (tECG) and the maternal component present in the aECG. Here the thoracic signal is the reference signal, and the abdominal signal is the desired signal to the adaptive filter. The proposed methodology uses the advantages of both multilayer perceptron (MLP) as well as functional link neural network (FLNN) in mapping the nonlinearity and effectively determining the fetal Electrocardiogram (fECG) from the aECG. For the detailed analysis, we have used the real Daisy database, the Non-invasive Fetal ECG database, and the fetal ECG synthetic database from Physionet. The results show that the nonlinear functional link MLP using the Volterra series gives a high-level performance compared to other classical adaptive filtering techniques, as all the evaluation metrics are above 90%.

Fetal monitoring technologies for the detection of intrapartum hypoxia - challenges and opportunities

Intrapartum fetal hypoxia is related to long-term morbidity and mortality of the fetus and the mother. Fetal surveillance is extremely important to minimize the adverse outcomes arising from fetal hypoxia during labour. Several methods have been used in current clinical practice to monitor fetal well-being. For instance, biophysical technologies including cardiotocography, ST-analysis adjunct to cardiotocography, and Doppler ultrasound are used for intrapartum fetal monitoring. However, these technologies result in a high false-positive rate and increased obstetric interventions during labour. Alternatively, biochemical-based technologies including fetal scalp blood sampling and fetal pulse oximetry are used to identify metabolic acidosis and oxygen deprivation resulting from fetal hypoxia. These technologies neither improve clinical outcomes nor reduce unnecessary interventions during labour. Also, there is a need to link the physiological changes during fetal hypoxia to fetal monitoring technologies. The objective of this article is to assess the clinical background of fetal hypoxia and to review existing monitoring technologies for the detection and monitoring of fetal hypoxia. A comprehensive review has been made to predict fetal hypoxia using computational and machine-learning algorithms. The detection of more specific biomarkers or new sensing technologies is also reviewed which may help in the enhancement of the reliability of continuous fetal monitoring and may result in the accurate detection of intrapartum fetal hypoxia.

Joint Improved Fast Independent Component Analysis and Singular Value Decomposition for Fetal Electrocardiogram Extraction

Combined the improved fast independent component analysis (FastICA) algorithm with the singular value decomposition algorithm, a single-channel fetal electrocardiogram (fECG) extraction method is proposed. First, the improved FastICA algorithm is used to estimate the maternal ECG component from a single-channel abdominal signal of pregnant women using an overrelaxation factor. Then, a preliminary estimate of the fECG signal is obtained by subtracting from the single-channel abdominal signal. Subsequently, the singular value decomposition algorithm is used to denoise the preliminarily estimated fECG signal to obtain a high signal-to-noise ratio. In addition, in the singular value decomposition algorithm for fetal arrhythmia, an improved method for constructing the ECG signal reconstruction matrix is proposed. Finally, the fECG extraction experiments on synthetic abdominal signals and actual abdominal signals (data from 49 abdominal channels sourced from DAISY database and the non-invasive fECG database in PhysioNet) are carried out. The experimental results show that the method in this paper can effectively improve the signal-to-noise ratio and the accuracy of fECG signal extraction, and is suitable for maternal or fetal arrhythmias. Compared with the FastICA algorithm, the signal-to-noise ratio of the fECG signal extracted by the method in this paper is improved by about 5 dB, and the accuracy of fECG extraction in the PhysioNet database can reach 96.54%.

A Novel Approach to Fetal ECG Extraction Using Temporal Convolutional Encoder-Decoder Network (TCED-Net)

To extract weak fetal ECG signals from the mixed ECG signal on the mother's abdominal wall, providing a basis for accurately estimating fetal heart rate and analyzing fetal ECG morphology. First, based on the relationship between the maternal chest ECG signal and the maternal ECG component in the abdominal signal, the temporal convolutional encoder-decoder network (TCED-Net) model is trained to fit the nonlinear transmission of the maternal ECG signal from the chest to the abdominal wall. Then, the maternal chest ECG signal is nonlinearly transformed to estimate the maternal ECG component in the abdominal mixed signal. Finally, the estimated maternal ECG component is subtracted from the abdominal mixed signal to obtain the fetal ECG component. The simulation results on the FECGSYN dataset show that the proposed approach achieves the best performance in F1 score, mean square error (MSE), and quality signal-to-noise ratio (qSNR) (98.94%, 0.18, and 8.30, respectively). On the NI-FECG dataset, although the fetal ECG component is small in energy in the mixed signal, this method can effectively suppress the maternal ECG component and thus extract a clearer and more accurate fetal ECG signal. Compared with existing algorithms, the proposed method can extract clearer fetal ECG signals, which has significant application value for effective fetal health monitoring during pregnancy.

New Features for the Detection of Fetal QRS Complexes in Non-Invasive Fetal Electrocardiograms

Non-invasive fetal electrocardiography (NI-fECG) is a promising technique for continuous fetal heart rate (fHR) monitoring. However, the weak amplitude of the fetal electrocardiogram (fECG), and the presence of the dominant maternal ECG (mECG), makes it highly challenging to detect the fetal QRS (fQRS) complex, which is needed to obtain the fHR. This paper proposes a new method for automated fQRS detection from single-channel NI-fECG signals, without cancelling out the mECG. The proposed method leverages the different spectral behaviour exhibited by mECG and fECG signals. Fetal R-peaks are detected using a hybrid combination of k-means clustering with time and time-frequency features extracted from pre-processed NI-fECG recordings. The performance of our method is evaluated using real and synthetic signals from publicly available datasets, achieving a best of 96.3% sensitivity and 90.4% F1 score. The results obtained demonstrates the effectiveness of the proposed method for the detection of fQRS complexes with high sensitivity and low computational complexity.

A signal quality assessment method for fetal QRS complexes detection

OBJECTIVE

Non-invasive fetal ECG (NI-FECG) provides a non-invasive method to monitor the health of the fetus. However, the NI-FECG is easily interfered by noise, which makes the signal quality decline, leading to the fetal heart rate (FHR) monitoring becoming a challenging task.

METHODS

In this work, an algorithm for dynamic evaluation of signal quality is proposed to improve the multi-channel FHR monitoring. The innovation of the method is to assess the signal quality in the process of multi-channel fetal QRS (FQRS) complexes detection. Specifically, the detected FQRS is used as quality unit. Each quality unit can be a true R peak (TR) or a false R peak (FR). It is the basic quality information in this work. The signal quality of each channel is estimated by estimating the correctness of the detection results. Further, the TRs of all channels can be fused to obtain more reliable fetal heart rate monitoring.

MAIN RESULTS

Analysis results demonstrate that the proposed algorithm is capable of selecting the good quality signal for FQRS detection achieving 97.40% PPV, 98.33% SE and 97.86% F₁.

SIGNIFICANCE

This work sheds light on the quality assessment of fetal monitoring signal.

Morphology extraction of fetal ECG using temporal CNN-based nonlinear adaptive noise cancelling

OBJECTIVES

Noninvasive fetal electrocardiography (FECG) offers many advantages over alternative fetal monitoring techniques in evaluating fetal health conditions. However, it is difficult to extract a clean FECG signal with morphological features from an abdominal ECG recorded at the maternal abdomen; the signal is usually contaminated by the maternal ECG and various noises. The aim of the work is to extract an FECG signal that preserves the morphological features from the mother's abdominal ECG recording, which allows for accurately estimating the fetal heart rate (FHR) and analyzing the waveforms of the fetal ECG.

METHODS

We propose a novel nonlinear adaptive noise cancelling framework (ANC) based on a temporal convolutional neural network (CNN) to effectively extract fetal ECG signals from mothers' abdominal ECG recordings. The proposed framework consists of a two-stage network, using the ANC architecture; one network is for the maternal ECG component elimination and the other is for the residual noise component removal of the extracted fetal ECG signal. Then, JADE (one of the blind source separation algorithms) is applied as a postprocessing step to produce a clean fetal ECG signal.

RESULTS

Synthetic ECG data (FECGSYNDB) and clinical ECG data (NIFECGDB, PCDB) are used to evaluate the extraction performance of the proposed framework. The statistical and visual results demonstrate that our method outperforms the other state-of-the-art algorithms in the literature. Specifically, on the FECGSYNDB, the mean squared error (MSE), signal-to-noise ratio (SNR), correlation coefficient (R) and F1-score of our method are 0.16, 7.94, 0.95 and 98.89%, respectively. The F1-score on the NIFECGDB reaches 98.62%. The value of the F1-score on the PCDB is 98.62%.

CONCLUSION

As opposed to the existing algorithms being restricted to fetal QRS complex detection, the proposed framework can preserve the morphological features of the extracted fetal ECG signal well, which could support medical diagnoses based on the morphology of the fetal ECG signal.

Associations between locus coeruleus MRI contrast and physiological responses to acute stress in younger and older adults

Acute stress activates the brain's locus coeruleus (LC)-noradrenaline system. Recent studies indicate that a magnetic resonance imaging (MRI)-based measure of LC structure is associated with better cognitive outcomes in later life. Yet despite the LC's documented role in promoting physiological arousal during acute stress, no studies have examined whether MRI-assessed LC structure is related to arousal responses to acute stress. In this study, 102 younger and 51 older adults completed an acute stress induction task while we assessed multiple measures of physiological arousal (heart rate, breathing rate, systolic and diastolic blood pressure, sympathetic tone, and heart rate variability, HRV). We used turbo spin echo MRI scans to quantify LC MRI contrast as a measure of LC structure. We applied univariate and multivariate approaches to assess how LC MRI contrast was associated with arousal at rest and during acute stress reactivity and recovery. In older participants, having higher caudal LC MRI contrast was associated with greater stress-related increases in systolic blood pressure and decreases in HRV, as well as lower HRV during recovery from acute stress. These results suggest that having higher caudal LC MRI contrast in older adulthood is associated with more pronounced physiological responses to acute stress. Further work is needed to confirm these patterns in larger samples of older adults.

A comparative study of the performance of methods for f-wave extraction

Objective.This study proposes a novel technique for atrial fibrillatory waves (f-waves) extraction and investigates the performance of the proposed method comparing with different f-wave extraction methods.Approach.We propose a novel technique combining a periodic component analysis (PiCA) and echo state network (ESN) for f-waves extraction, denoted PiCA-ESN. PiCA-ESN benefits from the advantages of using both source separation and nonlinear adaptive filtering. PiCA-ESN is evaluated by comparing with other state-of-the-art approaches, which include template subtraction technique based on principal component analysis, spatiotemporal cancellation, nonlinear adaptive filtering using an echo state neural network, and a source separation technique based on PiCA. Quality assessment is performed on a recently published reference database including a large number of simulated ECG signals in atrial fibrillation (AF). The performance of the f-wave extraction methods is evaluated in terms of signal quality metrics (SNR, ΔSNR) and robustness of f-wave features.Main results.The proposed method offers the best signal quality performance, with a ΔSNR of approximately 22 dB across all 8 sets of the reference database, as well as the most robust extraction of f-wave features, with 75% of all estimates of dominant atrial frequency well below 1 Hz.

Machine learning to support triage of children at risk for epileptic seizures in the pediatric intensive care unit

OBJECTIVE

Epileptic seizures are relatively common in critically-ill children admitted to the pediatric intensive care unit (PICU) and thus serve as an important target for identification and treatment. Most of these seizures have no discernible clinical manifestation but still have a significant impact on morbidity and mortality. Children that are deemed at risk for seizures within the PICU are monitored using continuous-electroencephalogram (cEEG). cEEG monitoring cost is considerable and as the number of available machines is always limited, clinicians need to resort to triaging patients according to perceived risk in order to allocate resources. This research aims to develop a computer aided tool to improve seizures risk assessment in critically-ill children, using an ubiquitously recorded signal in the PICU, namely the electrocardiogram (ECG).

APPROACH

A novel data-driven model was developed at a patient-level approach, based on features extracted from the first hour of ECG recording and the clinical data of the patient.

MAIN RESULTS

The most predictive features were the age of the patient, the brain injury as coma etiology and the QRS area. For patients without any prior clinical data, using one hour of ECG recording, the classification performance of the random forest classifier reached an area under the receiver operating characteristic curve (AUROC) score of 0.84. When combining ECG features with the patients clinical history, the AUROC reached 0.87.

SIGNIFICANCE

Taking a real clinical scenario, we estimated that our clinical decision support triage tool can improve the positive predictive value by more than 59% over the clinical standard.

Detecting beats in the photoplethysmogram: benchmarking open-source algorithms

The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat detection algorithms have been proposed, although it is not clear which performs best.

OBJECTIVE

This study aimed to: (i) develop a framework with which to design and test PPG beat detectors; (ii) assess the performance of PPG beat detectors in different use cases; and (iii) investigate how their performance is affected by patient demographics and physiology.

APPROACH

Fifteen beat detectors were assessed against electrocardiogram-derived heartbeats using data from eight datasets. Performance was assessed using the F1 score, which combines sensitivity and positive predictive value.

MAIN RESULTS

Eight beat detectors performed well in the absence of movement, with F1 scores of ≥90\% on hospital data and wearable data collected at rest. Their performance was poorer during exercise, with F1 scores of 55-91\%; poorer in neonates than adults with F1 scores of 84-96\% in neonates compared to 98-99\% in adults; and poorer in atrial fibrillation (AF), with F1 scores of 92-97\% in AF, compared to 99-100\% in normal sinus rhythm.

SIGNIFICANCE

Two PPG beat detectors denoted 'MSPTD' and 'qppg' performed best, with complementary performance characteristics. This evidence can be used to inform the choice of PPG beat detector algorithm. The algorithms, datasets, and assessment framework are freely available.

Validation of a new impedance cardiography analysis algorithm for clinical classification of stress states

Pre-ejection period (PEP) is an index of sympathetic nervous system activity that can be computed from electrocardiogram (ECG) and impedance cardiogram (ICG) signals, but sensitive to speech/motion artifact. We sought to validate an ICG noise removal method, three-stage ensemble-average algorithm (TEA), in data acquired from a clinical trial comparing active versus sham non-invasive vagal nerve stimulation (tcVNS) after standardized speech stress. We first compared TEA's performance versus the standard conventional ensemble-average algorithm (CEA) approach to classify noisy ICG segments. We then analyzed ECG and ICG data to measure PEP and compared group-level differences in stress states with each approach. We evaluated 45 individuals, of whom 23 had post-traumatic stress disorder (PTSD). We found that the TEA approach identified artifact-corrupted beats with intraclass correlation coefficient > 0.99 compared to expert adjudication. TEA also resulted in higher group-level differences in PEP between stress states than CEA. PEP values were lower in the speech stress (vs. baseline rest) group using both techniques, but the differences were greater using TEA (12.1 ms) than CEA (8.0 ms). PEP differences in groups divided by PTSD status and tcVNS (active vs. sham) were also greater when using the TEA versus CEA method, although the magnitude of the differences was lower. In conclusion, TEA helps to accurately identify noisy ICG beats during speaking stress, and this increased accuracy improves sensitivity to group-level differences in stress states compared to CEA, suggesting greater clinical utility.

Heart rate estimation and validation algorithm for fetal phonocardiography

Fetal heart rate (FHR) is an important parameter for assessing fetal well-being and is usually measured by doppler ultrasound. Fetal phonocardiography can provide non-invasive, easy-to-use and passive alternative for fetal monitoring method if reliable FHR measurements can be made even in noisy clinical environments. In this work we present an automatic algorithm to determine fetal heart rate from the fetal heart sound recordings in a noisy clinical environment. Using an electronic stethoscope fetal heart sounds were recorded from the expecting mother's abdomen, during weeks 30-40 of their pregnancy. Of these, 60 recordings were analyzed manually by two observers to obtain reference heart rate measurement. An algorithm was developed to determine FHR using envelope detection and autocorrelation of the signals. Algorithm performance was improved by implementing peak validation algorithm utilizing knowledge of valid FHR from prior windows and power spectral density function. The improvements in accuracy and reliability of algorithm were measured by mean absolute error and positive precent agreement. By including the validation step, the mean absolute error reduced from 11.50 to 7.54 beats per minute and positive percent agreement improved from 81% to 87%. The proposed algorithms provide good accuracy overall but are sensitive to the noises in recording environment that influence the quality of the signals.

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.

Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters of different hybrid systems used for non-invasive fetal electrocardiogram (fECG) extraction. The tested hybrid systems consist of two different blocks, first for maternal component estimation and second, so-called adaptive block, for maternal component suppression by means of an adaptive algorithm (AA). Herein, we tested and optimized four different AAs: Adaptive Linear Neuron (ADALINE), Standard Least Mean Squares (LMS), Sign-Error LMS, Standard Recursive Least Squares (RLS), and Fast Transversal Filter (FTF). The main criterion for optimal parameter selection was the F1 parameter. We conducted experiments using real signals from publicly available databases and those acquired by our own measurements. Our optimization method enabled us to find the corresponding optimal settings for individual adaptive block of all tested hybrid systems which improves achieved results. These improvements in turn could lead to a more accurate fetal heart rate monitoring and detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to find optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing and analysis, opening new diagnostic possibilities of non-invasive fetal electrocardiography.

An Autoencoder-Based Fetal Heart Rate Detector for Noninvasive Recordings

Antenatal fetal health monitoring primarily depends on the signal analysis of abdominal or transabdominal electrocardiogram (ECG) recordings. The noninvasive approach for obtaining fetal heart rate (HR) reduces risks of potential infections and is convenient for the expectant mother. However, in addition to strong maternal ECG presence, undesirable signals due to body motion activity, muscle contractions, and certain bio-electric potentials degrade the diagnostic quality of obtained fetal ECG from abdominal ECG recordings. In this paper, we address this problem by proposing an improved framework for estimating fetal HR from non-invasively acquired abdominal ECG recordings. Since the most significant contamination is due to maternal ECG, in the proposed framework, we rely on neural network autoencoder for reconstructing maternal ECG. The autoencoder endeavors to establish the nonlinear mapping between abdominal ECG and maternal ECG thus preserving inherent fetal ECG artifacts. The framework is supplemented with an existing blind-source separation (BSS) algorithm for post-treatment of residual signals obtained after subtracting reconstructed maternal ECG from abdominal ECG. Furthermore, experimental assessments on clinically-acquired subjects' recordings advocate the effectiveness of the proposed framework in comparison with conventional techniques for maternal ECG removal.

Fetal ECG extraction using short time Fourier transform and generative adversarial networks

Objective.Fetal ECG (FECG) plays an important role in fetal monitoring. However, the abdominal ECG (AECG) recorded at the maternal abdomen is affected by various noises, making the extraction of FECG a challenging task. The main objective is to present a novel approach to FECG extraction using short time Fourier transform (STFT) and generative adversarial networks (GAN).Methods.Firstly, the AECG signals are transformed from one-dimensional (1D) time domain to two-dimensional (2D) time-frequency domain by using the STFT. Secondly, the 2D-STFT coefficients of FECG are estimated by the GAN model in the time-frequency domain. Finally, after the inverse STFT, the FECG can be reconstructed in the time domain.Main results.Experimental results on two databases demonstrate the effectiveness of the proposed method. Specifically, the SE, PPV andF₁of the proposed method on PCDB are 92.37 ± 3.78%, 93.69 ± 3.96% and 93.02 ± 3.81%, respectively. And the SE, PPV andF₁on ADFECGDB are 90.32 ± 10.70%, 89.79 ± 9.26% and 90.05 ± 9.81%, respectively.Significance.Unlike the previous studies based on the elimination of maternal ECG in the 1D time domain, the novelty of the proposed method relies on extracting the FECG directly from the AECG in the 2D time-frequency domain. It sheds some light to the topic of FECG extraction.

Fetal ECG Extraction from Maternal ECG using Attention-based CycleGAN

A non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from the maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of the FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal to map the MECG to the FECG. The high correlation between maternal and fetal ECG fragments decreases the performance of convolution layers. Therefore, the masking region of interest based on the attention mechanism was performed to improve the signal generators' precision. The sine activation function was also used to retain more details when converting two signal domains. Three available datasets from the Physionet, including the A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, were used to evaluate the performance. The proposed method could map an abdominal MECG to a scalp FECG with an average of 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on the A&D FECG dataset. Moreover, it achieved 99.7 % F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on the A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. Also, the distortion was in the very good and good ranges. These results are comparable to the state-of-the-art results; thus, the proposed algorithm has the potential to be used for high-performance signal-to-signal conversion.

A signal quality assessment-based ECG waveform delineation method used for wearable monitoring systems

Identifying transient and nonpersistent abnormal electrocardiogram (ECG) waveforms by continuously monitoring the high-risk populations is of great importance for the diagnosis, treatment, and prevention of cardiovascular diseases. In recent years, fabric electrodes have been widely used in wearable devices because of their non-irritating properties and better comfort than traditional AgCl electrodes. However, the motion noise caused by the relative movement between the fabric electrodes and skin affects the quality of ECGs and reduces the accuracy of diagnosis. Therefore, delineating the ECG waveforms that are recorded from wearable devices with varying levels of noise is still challenging. In this study, a signal quality assessment (SQA)-based ECG waveform delineation method that is used for wearable systems was developed. The ECG signal was first preprocessed by a bandpass filter. Five indices, including the multiscale nonlinear amplitude statistical distribution (adSQI₁, adSQI₂), the proportion of energy-related to T wave (ptSQI), and heart rates computed from R waves and T waves (rHR and tHR, respectively), were then calculated from the preprocessed ECG signal. The signals were classified as good, acceptable, and unacceptable ECGs by combining these indices through the use of a neural network. Subsequently, the R waves or/and T waves were identified for the corresponding feature interpretations based on the SQA results. ECGs that were recorded from the chest belts from 29 volunteers at different activity statuses were divided into 4-s segments. A total of 7133 manually labeled segments were used to derive (4985 segments) and validate (2148 segments) the algorithm. The adSQI₁, adSQI₂, tHR, and rHR characteristics were significantly different among the good, acceptable, and unacceptable ECGs. The ptSQI value was considerably higher in the good ECGs than in the acceptable and unacceptable ECGs. The ECG segments of different quality levels were classified with an accuracy of 96.74% by using the proposed SQA method. The R waves and T waves were identified with accuracies of 99.95% and 99.57%, respectively, for segments that were classified as acceptable and/or good. The SQA-based ECG waveform delineation method can perform reliable analysis and has the potential to be applied in wearable ECG systems for the early diagnosis and prevention of cardiovascular diseases.

A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ECG extraction

Non-invasive fetal electrocardiography appears to be one of the most promising fetal monitoring techniques during pregnancy and delivery nowadays. This method is based on recording electrical potentials produced by the fetal heart from the surface of the maternal abdomen. Unfortunately, in addition to the useful fetal electrocardiographic signal, there are other interference signals in the abdominal recording that need to be filtered. The biggest challenge in designing filtration methods is the suppression of the maternal electrocardiographic signal. This study focuses on the extraction of fetal electrocardiographic signal from abdominal recordings using a combination of independent component analysis, recursive least squares, and ensemble empirical mode decomposition. The method was tested on two databases, the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations and the PhysioNet Challenge 2013 database. The evaluation was performed by the assessment of the accuracy of fetal QRS complexes detection and the quality of fetal heart rate determination. The effectiveness of the method was measured by means of the statistical parameters as accuracy, sensitivity, positive predictive value, and F1-score. Using the proposed method, when testing on the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database, accuracy higher than 80% was achieved for 11 out of 12 recordings with an average value of accuracy 92.75% [95% confidence interval: 91.19-93.88%], sensitivity 95.09% [95% confidence interval: 93.68-96.03%], positive predictive value 96.36% [95% confidence interval: 95.05-97.17%] and F1-score 95.69% [95% confidence interval: 94.83-96.35%]. When testing on the Physionet Challenge 2013 database, accuracy higher than 80% was achieved for 17 out of 25 recordings with an average value of accuracy 78.24% [95% confidence interval: 73.44-81.85%], sensitivity 81.79% [95% confidence interval: 76.59-85.43%], positive predictive value 87.16% [95% confidence interval: 81.95-90.35%] and F1-score 84.08% [95% confidence interval: 80.75-86.64%]. Moreover, the non-invasive ST segment analysis was carried out on the records from the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database and achieved high accuracy in 7 from in total of 12 records (mean values μ < 0.1 and values of ±1.96σ < 0.1).

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 ECG signal processing and identification of hypoxic pregnancy conditions in-utero

The fetal heart rate (fHR) variability and fetal electrocardiogram (fECG) are considered the most important sources of information about fetal wellbeing. Non-invasive fetal monitoring and analysis of fECG are paramount for clinical trials. They enable examining the fetal health status and detecting the heart rate changes associated with insufficient oxygenation to cut the likelihood of hypoxic fetal injury. Despite the fact that significant advances have been achieved in electrocardiography and adult ECG signal processing, the analysis of fECG is still in its infancy. Due to accurate fetal morphology extraction techniques have not been properly developed, many areas require particular attention on the way of fully understanding the changes in variability in the fetus and implementation of the non-invasive techniques suitable for remote home care which is increasingly in demand for high-risk pregnancy monitoring. In this paper, we introduce an integrated approach for fECG signal extraction and processing based on various methods for fetal welfare investigation and hypoxia risk estimation. To the best of our knowledge, this is the first attempt to introduce the auto-generated risk scoring in fECG to achieve early warning on fetus' safety and provide the physician with additional information about the possible fetal complications. The proposed method includes the following stages: fECG extraction, fHR and fetal heart rate variability (fHRV) calculation, hypoxia index (HI) evaluation and risk estimation. The extracted signals were examined by assessing Signal to Noise Ratio (SNR) and mean square error (MSE) values. The results obtained demonstrated great potential, but more profound research and validation, as well as a consistent clinical study, are needed before implementation into the hospital and at-home monitoring.

AECG-DecompNet: abdominal ECG signal decomposition through deep-learning model

Objective.The accurate decomposition of a mother's abdominal electrocardiogram (AECG) to extract the fetal ECG (FECG) is a primary step in evaluating the fetus's health. However, the AECG is often affected by different noises and interferences, such as the maternal ECG (MECG), making it hard to evaluate the FECG signal. In this paper, we propose a deep-learning-based framework, namely 'AECG-DecompNet', to efficiently extract both MECG and FECG from a single-channel abdominal electrode recording.Approach.AECG-DecompNet is based on two series networks to decompose AECG, one for MECG estimation and the other to eliminate interference and noise. Both networks are based on an encoder-decoder architecture with internal and external skip connections to reconstruct the signals better.Main results.Experimental results show that the proposed framework performs much better than utilizing one network for direct FECG extraction. In addition, the comparison of the proposed framework with popular single-channel extraction techniques shows superior results in terms of QRS detection while indicating its ability to preserve morphological information. AECG-DecompNet achieves exceptional accuracy in theprecisionmetric (97.4%), higher accuracy inrecallandF₁metrics (93.52% and 95.42% respectively), and outperforms other state-of-the-art approaches.Significance.The proposed method shows a notable performance in preserving the morphological information when the FECG within the AECG signal is weak.

An improved parallel sub-filter adaptive noise canceler for the extraction of fetal ECG

Non-invasive extraction of fetal electrocardiogram (FECG) by processing the abdominal signals is emerging as a promising approach in the areas of obstetrics and gynecology. This paper presents a two-stage improved non-linear adaptive filter for FECG extraction. The reference input to the adaptive noise canceler (ANC) is first processed using an adaptive neuro-fuzzy inference system (ANFIS) to estimate the non-linear maternal component in abdominal signals. A parallel sub-filter (PSF) ANC is proposed to assess the fetal ECG from the abdominal signal. The PSF-ANC decomposes a single adaptive filter into multiple sub-filters to improve the convergence performance. The filter coefficients of PSF-ANC adaptively obtained using normalised least mean square algorithm by minimizing the mean square error. Different error and common error algorithms are proposed based on the computation of the error signal. A synthetic data from the FECG synthetic database is used to evaluate the convergence performance. Two real-time data from the Daisy database and the Non-invasive FECG database from Physionet are used to evaluate the proposed ANFIS-PSF's performance qualitative and quantitatively. The results justify the performance improvement of proposed ANFIS-PSF ANC compared to the state of art techniques. The proposed scheme achieves a sensitivity of 97.92%, 94.52% accuracy, a positive predictive value of 94.66%, and an F1 score of 96.12%.

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 review of fetal cardiac monitoring, with a focus on low- and middle-income countries

There is limited evidence regarding the utility of fetal monitoring during pregnancy, particularly during labor and delivery. Developed countries rely on consensus ‘best practices’ of obstetrics and gynecology professional societies to guide their protocols and policies. Protocols are often driven by the desire to be as safe as possible and avoid litigation, regardless of the cost of downstream treatment. In high-resource settings, there may be a justification for this approach. In low-resource settings, in particular, interventions can be costly and lead to adverse outcomes in subsequent pregnancies. Therefore, it is essential to consider the evidence and cost of different fetal monitoring approaches, particularly in the context of treatment and care in low-to-middle income countries.

This article reviews the standard methods used for fetal monitoring, with particular emphasis on fetal cardiac assessment, which is a reliable indicator of fetal well-being. An overview of fetal monitoring practices in low-to-middle income counties, including perinatal care access challenges, is also presented. Finally, an overview of how mobile technology may help reduce barriers to perinatal care access in low-resource settings is provided.

WAUC: A Multi-Modal Database for Mental Workload Assessment Under Physical Activity

Assessment of mental workload is crucial for applications that require sustained attention and where conditions such as mental fatigue and drowsiness must be avoided. Previous work that attempted to devise objective methods to model mental workload were mainly based on neurological or physiological data collected when the participants performed tasks that did not involve physical activity. While such models may be useful for scenarios that involve static operators, they may not apply in real-world situations where operators are performing tasks under varying levels of physical activity, such as those faced by first responders, firefighters, and police officers. Here, we describe WAUC, a multimodal database of mental Workload Assessment Under physical aCtivity. The study involved 48 participants who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. Physical activity was manipulated by changing the speed of a stationary bike or a treadmill. During data collection, six neural and physiological modalities were recorded, namely: electroencephalography, electrocardiography, breathing rate, skin temperature, galvanic skin response, and blood volume pulse, in addition to 3-axis accelerometry. Moreover, participants were asked to answer the NASA Task Load Index questionnaire after each experimental section, as well as rate their physical fatigue level on the Borg fatigue scale. In order to bring our experimental setup closer to real-world situations, all signals were monitored using wearable, off-the-shelf devices. In this paper, we describe the adopted experimental protocol, as well as validate the subjective, neural, and physiological data collected. The WAUC database, including the raw data and features, subjective ratings, and scripts to reproduce the experiments reported herein will be made available at: http://musaelab.ca/resources/.

Annotated real and synthetic datasets for non-invasive foetal electrocardiography post-processing benchmarking

Non-invasive foetal electrocardiography (fECG) can be obtained at different gestational ages by means of surface electrodes applied on the maternal abdomen. The signal-to-noise ratio (SNR) of the fECG is usually low, due to the small size of the foetal heart, the foetal-maternal compartment, the maternal physiological interferences and the instrumental noise. Even after powerful fECG extraction algorithms, a post-processing step could be required to improve the SNR of the fECG signal. In order to support the researchers in the field, this work presents an annotated dataset of real and synthetic signals, which was used for the study “Wavelet Denoising as a Post-Processing Enhancement Method for Non-Invasive Foetal Electrocardiography” [1]. Specifically, 21 15 s-long fECG, dual-channel signals obtained by multi-reference adaptive filtering from real electrophysiological recordings were included. The annotation of the foetal R peaks by an expert cardiologist was also provided. Recordings were performed on 17 voluntary pregnant women between the 21st and the 27th week of gestation. The raw recordings were also included for the researchers interested in applying a different fECG extraction algorithm. Moreover, 40 10 s-long synthetic non-invasive fECG were provided, simulating the electrode placement of one of the abdominal leads used for the real dataset. The annotation of the foetal R peaks was also provided, as generated by the FECGSYN tool used for the signals’ creation. Clean fECG signals were also included for the computation of indexes of signal morphology preservation. All the signals are sampled at 2048 Hz. The data provided in this work can be used as a benchmark for fECG post-processing techniques but can also be used as raw signals for researchers interested in foetal QRS detection algorithms and fECG extraction methods.

Weighted Random Forests to Improve Arrhythmia Classification

Construction of an ensemble model is a process of combining many diverse base predictive learners. It arises questions of how to weight each model and how to tune the parameters of the weighting process. The most straightforward approach is simply to average the base models. However, numerous studies have shown that a weighted ensemble can provide superior prediction results to a simple average of models. The main goals of this article are to propose a new weighting algorithm applicable for each tree in the Random Forest model and the comprehensive examination of the optimal parameter tuning. Importantly, the approach is motivated by its flexibility, good performance, stability, and resistance to overfitting. The proposed scheme is examined and evaluated on the Physionet/Computing in Cardiology Challenge 2015 data set. It consists of signals (electrocardiograms and pulsatory waveforms) from intensive care patients which triggered an alarm for five cardiac arrhythmia types (Asystole, Bradycardia, Tachycardia, Ventricular Tachycardia, and Ventricular Fultter/Fibrillation). The classification problem regards whether the alarm should or should not have been generated. It was proved that the proposed weighting approach improved classification accuracy for the three most challenging out of the five investigated arrhythmias comparing to the standard Random Forest model.

Single-lead noninvasive fetal ECG extraction by means of combining clustering and principal components analysis

Early detection of potential hazards in the fetal physiological state during pregnancy and childbirth is very important. Noninvasive fetal electrocardiogram (FECG) can be extracted from the maternal abdominal signal. However, due to the interference of maternal electrocardiogram and other noises, the task of extraction is challenging. This paper introduces a novel single-lead noninvasive fetal electrocardiogram extraction method based on the technique of clustering and PCA. The method is divided into four steps: (1) pre-preprocessing; (2) fetal QRS complexes and maternal QRS complexes detection based on k-means clustering algorithm with the feature of max-min pairs; (3) FQRS correction step is to improve the performance of step two; (4) template subtraction based on PCA is introduced to extract FECG waveform. To verify the performance of the proposed algorithm, two clinical open-access databases are used to check the performance of FQRS detection. As a result, the method proposed shows the average PPV of 95.35%, Se of 96.23%, and F1-measure of 95.78%. Furthermore, the robustness test is carried out on an artificial database which proves that the algorithm has certain robustness in various noise environments. Therefore, this method is feasible and reliable to detect fetal heart rate and extract FECG. Graphical abstract Early detection of potential hazards in the fetal physiological state during pregnancy and childbirth is very important. Noninvasive fetal electrocardiogram (FECG) can be extracted from maternal abdominal signal. However, due to the interference of maternal electrocardiogram and other noises, the task of extraction is challenging. This paper introduces a novel single-lead noninvasive fetal electrocardiogram extraction method based on the technique of clustering and PCA. The method is divided into four steps: (1) pre-preprocessing; (2) fetal QRS complexes and maternal QRS complexes detection based on k-means clustering algorithm with the feature of max-min pairs; (3) FQRS correction step is to improve the performance of step two; (4) template subtraction based on PCA is introduced to extract FECG waveform. To verify the performance of algorithm, two clinical open-access databases are used to check the performance of FQRS detection. As a result, the method proposed shows the average PPV of 95.35%, Se of 96.23%, and F1-measure of 95.78%. Furthermore, the robustness test is carried out on an artificial database which proves that the algorithm has certain robustness in various noise environments. Therefore, this method is feasible and reliable to detect fetal heart rate and extract FECG.

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.

Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review

This paper presents a review of the techniques found in the literature that aim to achieve a robust heartbeat detection from fusing multi-modal physiological signals (e.g., electrocardiogram (ECG), blood pressure (BP), artificial blood pressure (ABP), stroke volume (SV), photoplethysmogram (PPG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), among others). Techniques typically employ ECG, BP, and ABP, of which usage has been shown to obtain the best performance under challenging conditions. SV, PPG, EMG, EEG, and EOG signals can help increase performance when included within the fusion. Filtering, signal normalization, and resampling are common preprocessing steps. Delay correction between the heartbeats obtained over some of the physiological signals must also be considered, and signal-quality assessment to retain the best signal/s must be considered as well. Fusion is usually accomplished by exploiting regularities in the RR intervals; by selecting the most promising signal for the detection at every moment; by a voting process; or by performing simultaneous detection and fusion using Bayesian techniques, hidden Markov models, or neural networks. Based on the results of the review, guidelines to facilitate future comparison of the performance of the different proposals are given and promising future lines of research are pointed out.

Fetal electrocardiography extraction with residual convolutional encoder-decoder networks

In the context of fetal monitoring, non-invasive fetal electrocardiography is an alternative approach to the traditional Doppler ultrasound technique. However, separating the fetal electrocardiography (FECG) component from the abdominal electrocardiography (AECG) remains a challenging task. This is mainly due to the interference from maternal electrocardiography, which has larger amplitude and overlaps with the FECG in both temporal and frequency domains. The main objective is to present a novel approach to FECG extraction by using a deep learning strategy from single-channel AECG recording. A residual convolutional encoder-decoder network (RCED-Net) is developed for this task of FECG extraction. The single-channel AECG recording is the input to the RCED-Net. And the RCED-Net extracts the feature of AECG and directly outputs the estimate of FECG component in the AECG recording. The AECG recordings from two different databases are collected to illustrate the efficiency of the proposed method. And the achieved results show that the proposed technique exhibits the best performance when compared to the existing methods in the literature. This work is a proof of concept that the proposed method could effectively extract the FECG component from AECG recordings. The focus on single-channel FECG extraction technique contributes to the commercial applications for long-term fetal monitoring.

QRStree: A prefix tree-based model to fetal QRS complexes detection

Non-invasive fetal electrocardiography (NI-FECG) plays an important role in fetal heart rate (FHR) measurement during the pregnancy. However, despite the large number of methods that have been proposed for adult ECG signal processing, the analysis of NI-FECG remains challenging and largely unexplored. In this study, we propose a prefix tree-based framework, called QRStree, for FHR measurement directly from the abdominal ECG (AECG). The procedure is composed of three stages: Firstly, a preprocessing stage is employed for noise elimination. Secondly, the proposed prefix tree-based method is used for fetal QRS complexes (FQRS) detection. Finally, a correction stage is applied for false positive and false negative correction. The novelty of the framework relies on using the range of FHR to establish the connections between the FQRS. The consecutive FQRS can be considered as strings composed of alphabet items, thus we can use the prefix tree to store them. A vertex of the tree contains an alphabet, thus a path of the tree gives a string. Such that, by storing the connections of the FQRS into the prefix tree structure, the problem of FQRS detection converts to a problem of optimal path selection. Specifically, after selecting the optimal path of the tree, the nodes in the optimal path are collected as detected FQRS. Since the prefix tree can cover every possible combination of the FQRS candidates, it has the potential to reduce the occurrence of miss detections. Results on two different databases show that the proposed method is effective in FHR measurement from single-channel AECG. The focus on single-channel FHR measurement facilitates the long-term monitoring for healthcare at home.

A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography

Fetal electrocardiography (fECG) is a promising alternative to cardiotocography continuous fetal monitoring. Robust extraction of the fetal signal from the abdominal mixture of maternal and fetal electrocardiograms presents the greatest challenge to effective fECG monitoring. This is mainly due to the low amplitude of the fetal versus maternal electrocardiogram and to the non-stationarity of the recorded signals. In this review, we highlight key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources (databases and source code). In particular, we highlight the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance. Improving or combining the current or developing new advanced signal processing methods may enable morphological analysis of the fetal electrocardiogram, which today is only possible using the invasive scalp electrocardiography method.

Temporally Nonstationary Component Analysis; Application to Noninvasive Fetal Electrocardiogram Extraction

OBJECTIVE

Mixtures of temporally nonstationary signals are very common in biomedical applications. The nonstationarity of the source signals can be used as a discriminative property for signal separation. Herein, a semi-blind source separation algorithm is proposed for the extraction of temporally nonstationary components from linear multichannel mixtures of signals and noises.

METHODS

A hypothesis test is proposed for the detection and fusion of temporally nonstationary events, by using ad hoc indexes for monitoring the first and second order statistics of the innovation process. As proof of concept, the general framework is customized and tested over noninvasive fetal cardiac recordings acquired from the maternal abdomen, over publicly available datasets, using two types of nonstationarity detectors: 1) a local power variations detector, and 2) a model-deviations detector using the innovation process properties of an extended Kalman filter.

RESULTS

The performance of the proposed method is assessed in presence of white and colored noise, in different signal-to-noise ratios.

CONCLUSION AND SIGNIFICANCE

The proposed scheme is general and it can be used for the extraction of nonstationary events and sample deviations from a presumed model in multivariate data, which is a recurrent problem in many machine learning applications.

Falk T. Multi-Scale Heart Beat Entropy Measures for Mental Workload Assessment of Ambulant Users

Mental workload assessment is crucial in many real life applications which require constant attention and where imbalance of mental workload resources may cause safety hazards. As such, mental workload and its relationship with heart rate variability (HRV) have been well studied in the literature. However, the majority of the developed models have assumed individuals are not ambulant, thus bypassing the issue of movement-related electrocardiography (ECG) artifacts and changing heart beat dynamics due to physical activity. In this work, multi-scale features for mental workload assessment of ambulatory users is explored. ECG data was sampled from users while they performed different types and levels of physical activity while performing the multi-attribute test battery (MATB-II) task at varying difficulty levels. Proposed features are shown to outperform benchmark ones and further exhibit complementarity when used in combination. Indeed, results show gains over the benchmark HRV measures of 24.41% in accuracy and of 27.97% in F1 score can be achieved even at high activity levels.

Signal Quality for RR Interval Prediction on Wearable Sensors

Physiological responses are essential for health monitoring. Wearable devices are providing greater populations of people with the ability to monitor physiological signals during their day to day activities. However, wearable devices are particularly susceptible to degradation of signal quality due to noise from motion artifacts, environment, and user error. In this paper, we compare the impact of including signal quality on predictive models for RR intervals in a real world setting.

A low-complexity photoplethysmographic systolic peak detector for compressed sensed data

OBJECTIVE

Recent advances in wearable technologies and signal processing have made it possible to perform health monitoring during everyday life activities. Despite the fact that new technologies allow the storage of large volumes of data on small devices, limitations remain when data have to be transmitted or processed with devices with both energy and computational constraints.

APPROACH

This work focuses on the implementation and validation of a photoplethysmogram (PPG) low-complexity analysis method for sensors that acquire a compressed PPG signal through compressive sensing (CS) and allows for the accurate detection of the PPG systolic peak in the compressed domain. Three public datasets were used consisting of a total of about 52 h of PPG signals from 600 patients with normal and abnormal rhythms. Peaks were manually annotated by experts or derived from the annotated synchronized ECG.

MAIN RESULTS

The proposed method achieved a pooled average F1 measure on the three datasets of 91% [Formula: see text] 8% for a 5% compression ratio (CR), 89% [Formula: see text] 10% for CR  =  70% and 82% [Formula: see text] 12% for CR of 90%. The pooled average F1 measure on the original uncompressed data using an offline open source peak detector is F1  =  91% [Formula: see text] 11%. The proposed method is up to  ∼100 times faster with respect to methods using decompression followed by peak detection.

SIGNIFICANCE

Results demonstrate that it is possible to achieve detection performance, in terms of the F1 measure, comparable with those obtained on the original uncompressed and filtered signal, making the proposed approach appropriate for real-time wearable systems with energy and computation constraints.

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

BACKGROUND AND OBJECTIVE

This 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.

METHODS

The 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 (DB_I), Physiobank challenge 2013 (DB_II) and abdominal and direct foetal ECG database (adfecgdb) of Physiobank (DB_III).

RESULTS

The algorithm detected no false positives or false negatives with certain channel for DB_I, DB_II and DB_III which shows that the proposed algorithm can achieve good performance. Overall accuracy and sensitivity of the system is 98.53% and 100% for DB_I. Best accuracy and sensitivity of 97.77% and 98.63% are obtained for DB_II. Best accuracy of 92.41% and sensitivity of 93.8% are obtained for DB_III. Correlation coefficient between actual foetal heart rate (fHR) and estimated fHR of 0.66 for DB_II and 0.59 for DB_III is obtained. The method has obtained overall F1 score of 99.25% for DB_I, 96.04% for DB_II and 94.25% for DB_III. It has obtained a best MSE of fHR and overall MSE of R-R interval which is 10.8bpm² and 2.2 ms for DB_II, 12bpm² and 2.14 ms for DB_III.

CONCLUSION

The 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.