Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data

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.

Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings

Objective.Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts.Approach.In this work, we proposePower-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmarkPower-MFagainst three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA).Main results.Our results show thatPower-MFoutperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5% ± 0.5% F1-score, B2 Labour: 98.0% ± 3.0% F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise.Significance.Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.

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.

A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision

Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases. Using the proposed approach, high F1-scores of 99.80% and 99.64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99.88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99.39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.

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.

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.

Fetal Movement Cancellation in Abdominal Electrocardiogram Recordings Using Signal-to-Signal Translation

This study addresses the cancellation of fetal movement in abdominal electrocardiogram (AECG) recordings through deep neural networks. For this purpose, a generative signal-to-signal translation model consisting of two coupled generators is employed to discover the relations between fetal movement-contaminated and clean AECG recordings. The model is trained on the fetal ECG synthetic database (FECGSYNDB) which provides AECG recordings from 10 pregnancies along with their ground-truth maternal and fetal ECG signals. The signals are initially segmented into 4-second segments and then fed into the network for denoising. It is demonstrated that the signal-to-signal translation method can reconstruct clean AECG signals with average mean-absolute-error (MAE), root-mean-square deviation (RMSD), and Pearson correlation coefficient (PCC) of 0.099, 0.124, and 99.12% respectively, between clean and denoised AECG signals. Furthermore, the robustness of the method to low signal-to-noise ratio (SNR) input values is shown by an RMSD range of (0.047, 0.352) for SNR values within the range of (-3, 3) dB. Clinical Relevance- The proposed framework allows for the denoising of abdominal ECG signals for non-invasive fetal heart rate monitoring. The approach is accurate due to the use of advanced neural network techniques.

Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording

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

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.

Frequency-Based Maternal Electrocardiogram Attenuation for Fetal Electrocardiogram Analysis

Fetal electrocardiogram (ECG) waveform analysis along with cardiac time intervals (CTIs) measurements are critical for the management of high-risk pregnancies. Currently, there is no system that can consistently and accurately measure fetal ECG. In this work, we present a new automatic approach to attenuate the maternal ECG in the frequency domain and enhance it with measurable CTIs. First, the coherent components between the maternal ECG and abdominal ECG were identified and subtracted from the latter in the frequency domain. The residual was then converted into the time domain using the inverse Fourier transform to yield the fetal ECG. This process was improved by averaging multiple beats. Two fetal cardiologists, blinded to the method, assessed the quality of fetal ECG based on a grading system and measured the CTIs. We evaluated the fetal ECG quality of our method and time-based methods using one synthetic dataset, one human dataset available in the public domain, and 37 clinical datasets. Among the 37 datasets analyzed, the mean average (± standard deviation) grade was 3.49 ± 1.22 for our method vs. 2.64 ± 1.26 for adaptive interference cancellation (p-value < 0.001), thus showing the frequency-based fetal ECG extraction was the superior method, as assessed from our clinicians' perspectives. This method has the potential for use in clinical settings.

Fetal Electrocardiogram Extraction from the Mother's Abdominal Signal Using the Ensemble Kalman Filter

Fetal electrocardiogram (fECG) assessment is essential throughout pregnancy to monitor the wellbeing and development of the fetus, and to possibly diagnose potential congenital heart defects. Due to the high noise incorporated in the abdominal ECG (aECG) signals, the extraction of fECG has been challenging. And it is even a lot more difficult for fECG extraction if only one channel of aECG is provided, i.e., in a compact patch device. In this paper, we propose a novel algorithm based on the Ensemble Kalman filter (EnKF) for non-invasive fECG extraction from a single-channel aECG signal. To assess the performance of the proposed algorithm, we used our own clinical data, obtained from a pilot study with 10 subjects each of 20 min recording, and data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology shows the average positive predictive value (PPV) of 97.59%, sensitivity (SE) of 96.91%, and F1-score of 97.25% from the PhysioNet 2013 Challenge bank. Our results also indicate that the proposed algorithm is reliable and effective, and it outperforms the recently proposed extended Kalman filter (EKF) based algorithm.

Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling

Objective:Fetal arrhythmias are a life-threatening disorder occurring in up to 2% of pregnancies. If identified, many fetal arrhythmias can be effectively treated using anti-arrhythmic therapies. In this paper, we present a novel method of detecting fetal arrhythmias in short length non-invasive fetal electrocardiography (NI-FECG) recordings.Approach:Our method consists of extracting a fetal heart rate (FHR) time series from each NI-FECG recording and computing an entropy profile using a data-driven range of the entropy tolerance parameter r. To validate our approach, we apply our entropy profiling method to a large clinical data set of 318 NI-FECG recordings.Main Results:We demonstrate that our method (TotalSampEn) provides strong performance for classifying arrhythmic fetuses (AUC of 0.83) and outperforms entropy measures such as SampEn (AUC of 0.68) and FuzzyEn (AUC of 0.72). We also find that NI-FECG recordings incorrectly classified using the investigated entropy measures have significantly lower signal quality, and that excluding recordings of low signal quality (13.5% of recordings) increases the classification accuracy of TotalSampEn (AUC of 0.90).Significance:The superior performance of our approach enables automated detection of fetal arrhythmias and warrants further investigation in a prospective clinical trial.

A Novel Cluster-Based Method for Single-channel Fetal Electrocardiogram Detection

Fetal electrocardiography (FECG) is a promising technology for non-invasive fetal monitoring. However, due to the low amplitude and non-stationary characteristics of the FECG signal, it is difficult to extract it from maternal abdominal signals. Moreover, most FECG extraction methods are based on multiple channels, which make it difficult to achieve fetal monitoring outside the clinic. This paper proposes an efficient cluster-based method for accurate FECG extraction and fetal QRS detection only using one channel signal. We designed min-max-min group as the basis for feature extraction. The extracted features are used to distinguish the different components of the abdominal signal, and finally extract the FECG signal. To verify the effectiveness of our algorithm, we conducted experiments on a public dataset and a dataset record from the Tongji Hospital. Experimental results show that our method can achieve an accuracy rate of more than 96% which is better than other algorithms.

Swarm Decomposition of Abdominal Signals for Non-invasive Fetal ECG Extraction

The non-invasive fetal electrocardiography (fECG) extraction from maternal abdominal signals is one of the most promising modern fetal monitoring techniques. However, the noninvasive fECG signal is heavily contaminated with noise and overlaps with other prominent signals like the maternal ECG. In this work we propose a novel approach in non-invasive fECG extraction using the swarm decomposition (SWD) to isolate the fetal components from the abdominal signal. Accompanied with the use of higher-order statistics (HOS) for R peak detection, the application of the proposed method to the Abdominal and Direct Fetal ECG PhysioNet Database resulted in fetal R peak detection sensitivity of 99.8% and a positive predictability of 99.8%. Our results demonstrate the applicability of SWD and its potentiality in extracting fECG of good morphological quality with more deep decomposition levels, in order to connect the extracted structural characteristics of the fECG with the health status of the fetus.Clinical Relevance- The developed method shows improvement in fetal R peak detection for certain signals.

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 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 dilated inception CNN-LSTM network for fetal heart rate estimation

Objective. Fetal heart rate (HR) monitoring is routinely used during pregnancy and labor to assess fetal well-being. The noninvasive fetal electrocardiogram (ECG), obtained by electrodes on the maternal abdomen, is a promising alternative to standard fetal monitoring. Subtraction of the maternal ECG from the abdominal measurements results in fetal ECG signals, in which the fetal HR can be determined typically through R-peak detection. However, the low signal-to-noise ratio and the nonstationary nature of the fetal ECG make R-peak detection a challenging task.Approach. We propose an alternative approach that instead of performing R-peak detection employs deep learning to directly determine the fetal HR from the extracted fetal ECG signals. We introduce a combination of dilated inception convolutional neural networks (CNN) with long short-term memory networks to capture both short-term and long-term temporal dynamics of the fetal HR. The robustness of the method is reinforced by a separate CNN-based classifier that estimates the reliability of the outcome.Main results. Our method achieved a positive percent agreement (within 10% of the actual fetal HR value) of 97.3% on a dataset recorded during labor and 99.6% on set-A of the 2013 Physionet/Computing in Cardiology Challenge exceeding top-performing state-of-the-art algorithms from the literature.Significance. The proposed method can potentially improve the accuracy and robustness of fetal HR extraction in clinical practice.

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.

Joint Multiple Subspace-based BSS Method for Fetal Heart Rate Extraction from Non-invasive Recordings

Despite the enormous potential applications, non-invasive recordings have not yet made enough satisfaction for fetal disease detection. This is mainly due to the fetal ECG signal is contaminated by the maternal electrocardiograph (ECG) interference, muscle contractions, and motion artifacts. In this paper, we propose a joint multiple subspace-based blind source separation (BSS) approach to extract the fetal heart rate (HR), so that it could greatly reduce the effect of maternal ECG and motion artifacts. The approach relies on the estimation of the coefficient matrix formulated as the tensor decomposition in terms of multiple datasets. Since the objective function takes the coupling information from the stacking of the covariance matrix for multiple datasets into account, estimating the coefficient matrices is fulfilled not only on dependence across multiple datasets, but also can combine the extracted components across four different datasets. Numerical results demonstrate that the proposed method can achieve a high extracted HR accuracy for each dataset, when compared to some conventional methods.

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

BACKGROUND AND OBJECTIVE

The detection of a clean and undistorted foetal electrocardiogram (fECG) from non-invasive abdominal recordings is an open research issue. Several physiological and instrumental noise sources hamper this process, even after that powerful fECG extraction algorithms have been used. Wavelet denoising is widely used for the improvement of the SNR in biomedical signal processing. This work aims to systematically assess conventional and unconventional wavelet denoising approaches for the post-processing of fECG signals by providing evidence of their effectiveness in improving fECG SNR while preserving the morphology of the signal of interest.

METHODS

The stationary wavelet transform (SWT) and the stationary wavelet packet transform (SWPT) were considered, due to their different granularity in the sub-band decomposition of the signal. Three thresholds from the literature, either conventional (Minimax and Universal) and unconventional, were selected. To this aim, the unconventional one was adapted for the first time to SWPT by trying different approaches. The decomposition depth was studied in relation to the characteristics of the fECG signal. Synthetic and real datasets, publicly available for benchmarking and research, were used for quantitative analysis in terms of noise reduction, foetal QRS detection performance and preservation of fECG morphology.

RESULTS

The adoption of wavelet denoising approaches generally improved the SNR. Interestingly, the SWT methods outperformed the SWPT ones in morphology preservation (p<0.04) and SNR (p<0.0003), despite their coarser granularity in the sub-band analysis. Remarkably, the Han et al. threshold, adopted for the first time for fECG processing, provided the best quality improvement (p<0.003).

CONCLUSIONS

The findings of our systematic analysis suggest that particular care must be taken when selecting and using wavelet denoising for non-invasive fECG signal post-processing. In particular, despite the general noise reduction capability, signal morphology can be significantly altered on the basis of the parameterization of the wavelet methods. Remarkably, the adoption of a finer sub-band decomposition provided by the wavelet packet was not able to improve the quality of the processing.

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.

Investigation of Methods to Extract Fetal Electrocardiogram from the Mother's Abdominal Signal in Practical Scenarios

Monitoring of fetal electrocardiogram (fECG) would provide useful information about fetal wellbeing as well as any abnormal development during pregnancy. Recent advances in flexible electronics and wearable technologies have enabled compact devices to acquire personal physiological signals in the home setting, including those of expectant mothers. However, the high noise level in the daily life renders long-entrenched challenges to extract fECG from the combined fetal/maternal ECG signal recorded in the abdominal area of the mother. Thus, an efficient fECG extraction scheme is a dire need. In this work, we intensively explored various extraction algorithms, including template subtraction (TS), independent component analysis (ICA), and extended Kalman filter (EKF) using the data from the PhysioNet 2013 Challenge. Furthermore, the modified data with Gaussian and motion noise added, mimicking a practical scenario, were utilized to examine the performance of algorithms. Finally, we combined different algorithms together, yielding promising results, with the best performance in the F1 score of 92.61% achieved by an algorithm combining ICA and TS. With the data modified by adding different types of noise, the combination of ICA-TS-ICA showed the highest F1 score of 85.4%. It should be noted that these combined approaches required higher computational complexity, including execution time and allocated memory compared with other methods. Owing to comprehensive examination through various evaluation metrics in different extraction algorithms, this study provides insights into the implementation and operation of state-of-the-art fetal and maternal monitoring systems in the era of mobile health.

Comparison of Single- and Multi-reference QRD-RLS adaptive filter for non-invasive fetal electrocardiography

Non-invasive fetal electrocardiography (ECG) would allow accessing very relevant information on fetal cardiac function, especially for arrhythmias. However, the signal-to-noise ratio is significantly low, since fetal ECG is embedded in instrumental noise and spectrally overlapping maternal electrophysiological interferences. Among the different techniques proposed in the scientific literature, some variants of adaptive filters have been proposed for maternal ECG cancellation and fetal QRS complex enhancement. Such techniques encompass approaches using one or more reference signals, which is an important aspect for the development of accurate and unobtrusive monitoring systems.In this work, this aspect is systematically analyzed by comparing single- and multi-reference implementations of the QRD-RLS adaptive filter, and by challenging them in the fetal ECG enhancement on three abdominal leads differently oriented in space. The performance is assessed on real data in terms of signal-to-interference ratio, detection of fetal QRS complexes and maternal ECG attenuation. Multi-reference implementation reveals its superiority, whereas the single-reference implementation suffers from the electrodes positioning and cannot be trustily used even for the fetal heart rate only on the adopted dataset.

A feasibility study on non-invasive fetal ECG to evaluate prenatal autonomic nervous system activity

BACKGROUND

Maturity of the autonomic nervous system (ANS) is of paramount importance for fetal adaptation to extrauterine life and for early neurological development. Markers of ANS maturity, such as electrophysiological heart rate parameters, are of interest as tools to determine prenatal fetal maturity. The available technology, fetal magnetocardiography is expensive and not suitable for clinical use. Detection of fetal electrocardiographic signals using traditional ECG leads on the maternal abdomen may be brought to the bedside, but is technically challenging. Our group has recently developed an innovative system consisting of a standard ECG with external leads applied on the maternal abdomen coupled with a software that extracts the fetal heart signal from the maternal noise.

OBJECTIVE

To validate the use of this innovative non-invasive system to detect fetal ECG (fECG) and its ability to detect changes in electrophysiological fetal cardiac parameters associated with ANS maturation.

STUDY DESIGN

we recruited 50 pregnant women between 24 and 41 weeks and they received non-invasive recording of fECG.

RESULTS

fECG was measurable at all gestational ages. Fetal heart rate variability (RR interval) and other associated parameters, such as low and high frequency increased with gestational age, particularly up to the 31st week.

CONCLUSIONS

This study shows that non-invasive fECG is feasible throughout a broad range of gestational ages and allows detecting electrophysiological parameters of the fetal heart that may be used a surrogate of ANS maturity. Technological implementation of this system and its further exploitation may generate new tool to estimate fetal maturity.

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.

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.

Noninvasive fetal electrocardiography for the detection of fetal arrhythmias

OBJECTIVE

To assess whether noninvasive fetal electrocardiography (NI-FECG) enables the diagnosis of fetal arrhythmias.

METHODS

A total of 500 echocardiography and NI-FECG recordings were collected from pregnant women during a routine medical visit in this multicenter study. All the cases with fetal arrhythmias (n = 12) and a matching number of control (n = 14) were used. Two perinatal cardiologists analyzed the extracted NI-FECG while blinded to the echocardiography. The NI-FECG-based diagnosis was compared with the reference fetal echocardiography diagnosis.

RESULTS

NI-FECG and fetal echocardiography agreed on all cases (Ac = 100%) on the presence of an arrhythmia or not. However, in one case, the type of arrhythmia identified by the NI-FECG was incorrect because of the low resolution of the extracted fetal P-wave, which prevented resolving the mechanism (2:1 atrioventricular conduction) of the atrial tachycardia.

CONCLUSION

It is possible to diagnose fetal arrhythmias using the NI-FECG technique. However, this study identifies that improvement in algorithms for reconstructing the P-wave is critical to systematically resolve the mechanisms underlying the arrhythmias. The elaboration of a NI-FECG Holter device will offer new opportunities for fetal diagnosis and remote monitoring of problematic pregnancies because of its low-cost, noninvasiveness, portability, and minimal setup requirements.

PhysioZoo: A Novel Open Access Platform for Heart Rate Variability Analysis of Mammalian Electrocardiographic Data

Background: The time variation between consecutive heartbeats is commonly referred to as heart rate variability (HRV). Loss of complexity in HRV has been documented in several cardiovascular diseases and has been associated with an increase in morbidity and mortality. However, the mechanisms that control HRV are not well understood. Animal experiments are the key to investigating this question. However, to date, there are no standard open source tools for HRV analysis of mammalian electrocardiogram (ECG) data and no centralized public databases for researchers to access. Methods: We created an open source software solution specifically designed for HRV analysis from ECG data of multiple mammals, including humans. We also created a set of public databases of mammalian ECG signals (dog, rabbit and mouse) with manually corrected R-peaks (>170,000 annotations) and signal quality annotations. The platform (software and databases) is called PhysioZoo. Results: PhysioZoo makes it possible to load ECG data and perform very accurate R-peak detection (F 1 > 98%). It also allows the user to manually correct the R-peak locations and annotate low signal quality of the underlying ECG. PhysioZoo implements state of the art HRV measures adapted for different mammals (dogs, rabbits, and mice) and allows easy export of all computed measures together with standard data representation figures. PhysioZoo provides databases and standard ranges for all HRV measures computed on healthy, conscious humans, dogs, rabbits, and mice at rest. Study of these measures across different mammals can provide new insights into the complexity of heart rate dynamics across species. Conclusion: PhysioZoo enables the standardization and reproducibility of HRV analysis in mammalian models through its open source code, freely available software, and open access databases. PhysioZoo will support and enable new investigations in mammalian HRV research. The source code and software are available on www.physiozoo.com.

Robust heartbeat detection using multimodal recordings and ECG quality assessment with signal amplitudes dispersion

BACKGROUND AND OBJECTIVES

The electrocardiogram (ECG) is a bioelectric signal which represents heart's electrical activity graphically. This bioelectric signal is subject of lots of researches and so many algorithms are designed for extracting lots of clinically important parameters from it. Most of these parameters can be measured by detecting R peak of the QRS complex in ECG signal, but when ECG signal is corrupted by different kinds of noise and artifacts, such as electromyogram (EMG) from muscles, power line interference, motion artifacts and changes in electrode-skin interface, detection of R peaks becomes hard or impossible for algorithms which are designed for heart beat detection on ECG signal. In modern patient monitoring devices often not only one ECG signal is recorded but also so many other biological signals are simultaneously recorded from the patient which some of them, such as blood pressure (BP), are containing useful information about the heart activity which could be very helpful in making the heart beat detection more robust.

METHODS

In this study, a new method is introduced for distinguishing noise free segments of ECG from noisy segments that uses samples amplitudes dispersion with an adaptive threshold for variance of samples amplitude and a method which uses compatibility of detected beats in ECG and some of other signals which are related to the heart activity such as BP, arterial pressure (ART) and pulmonary artery pressure (PAP). A prioritization is applied in other pulsatile signals based on the amplitude and clarity of peaks on them, and a fusion strategy is employed for segments on which ECG is noisy and other available signals in the data, which contain peaks corresponding to R peak of the ECG, are scored in a three steps scoring function.

RESULTS

The final scores achieved by the proposed algorithm in terms of average sensitivity, positive predictive value, accuracy and F1 measure on the database which is freely available in Physionet Computing in Cardiology Challenge 2014 are respectively 95.47%, 96.03%, 93.11% and 95.62%.

CONCLUSIONS

The results show the outperformance of the proposed method against other recently published works.

Real-time fetal ECG extraction from multichannel abdominal ECG using compressive sensing and ICA

An improved method for separation of fetal electrocardiogram (fECG) from abdominal electrocardiogram (abdECG) is proposed in this paper. Proposed method combines two widely used techniques i.e. compressive sensing (CS) and independent component analysis (ICA). Separation of fECG is carried out by applying ICA directly on the compressed signal. The efficient improved ℓ_p-regularized least-sqaures (ℓ_p-RLS) algorithm is used for signal reconstruction, which provides better reconstruction quality and less processing time in comparison with other existing methods. The proposed algorithm is evaluated and tested on Physionet datasets which contain 75 records in set-A, 100 records in set-B and 6 records in Silesia dataset. The obtained outcomes reveal that proposed algorithm shows promising results (Sensitivity S=92%, Positive predictivity P+ = 93%, F1 measure = 92.5% with average percentage root-mean-square difference PRD =6.89% and Execution time= 2.91 sec.). The results also indicate that there is a substantial improvement in quality of reconstructed signal which is achieved by maintaining lowest PRD.

A Universal Scaling Relation for Defining Power Spectral Bands in Mammalian Heart Rate Variability Analysis

Background: Power spectral density (PSD) analysis of the heartbeat intervals in the three main frequency bands [very low frequency (VLF), low frequency (LF), and high frequency (HF)] provides a quantitative non-invasive tool for assessing the function of the cardiovascular control system. In humans, these frequency bands were standardized following years of empirical evidence. However, no quantitative approach has justified the frequency cutoffs of these bands and how they might be adapted to other mammals. Defining mammal-specific frequency bands is necessary if the PSD analysis of the HR is to be used as a proxy for measuring the autonomic nervous system activity in animal models. Methods: We first describe the distribution of prominent frequency peaks found in the normalized PSD of mammalian data using a Gaussian mixture model while assuming three components corresponding to the traditional VLF, LF and HF bands. We trained the algorithm on a database of human electrocardiogram recordings (n = 18) and validated it on databases of dogs (n = 17) and mice (n = 8). Finally, we tested it to predict the bands for rabbits (n = 4) for the first time. Results: Double-logarithmic analysis demonstrates a scaling law between the GMM-identified cutoff frequencies and the typical heart rate (HRm): fVLF-LF = 0.0037⋅ HR m 0.58 , fLF-HF = 0.0017⋅ HR m 1.01 and fHFup = 0.0128⋅ HR m 0.86 . We found that the band cutoff frequencies and Gaussian mean scale with a power law of 1/4 or 1/8 of the typical body mass (BMm), thus revealing allometric power laws. Conclusion: Our automated data-driven approach allowed us to define the frequency bands in PSD analysis of beat-to-beat time series from different mammals. The scaling law between the band frequency cutoffs and the HRm can be used to approximate the PSD bands in other mammals.

A Novel Blaschke Unwinding Adaptive-Fourier-Decomposition-Based Signal Compression Algorithm With Application on ECG Signals

This paper presents a novel signal compression algorithm based on the Blaschke unwinding adaptive Fourier decomposition (AFD). The Blaschke unwinding AFD is a newly developed signal decomposition theory. It utilizes the Nevanlinna factorization and the maximal selection principle in each decomposition step, and achieves a faster convergence rate with higher fidelity. The proposed compression algorithm is applied to the electrocardiogram signal. To assess the performance of the proposed compression algorithm, in addition to the generic assessment criteria, we consider the less discussed criteria related to the clinical needs-for the heart rate variability analysis purpose, how accurate the R-peak information is preserved is evaluated. The experiments are conducted on the MIT-BIH arrhythmia benchmark database. The results show that the proposed algorithm performs better than other state-of-the-art approaches. Meanwhile, it also well preserves the R-peak information.

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

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

Performance Analysis of Ten Common QRS Detectors on Different ECG Application Cases

A systematical evaluation work was performed on ten widely used and high-efficient QRS detection algorithms in this study, aiming at verifying their performances and usefulness in different application situations. Four experiments were carried on six internationally recognized databases. Firstly, in the test of high-quality ECG database versus low-quality ECG database, for high signal quality database, all ten QRS detection algorithms had very high detection accuracy (F1 >99%), whereas the F1 results decrease significantly for the poor signal-quality ECG signals (all <80%). Secondly, in the test of normal ECG database versus arrhythmic ECG database, all ten QRS detection algorithms had good F1 results for these two databases (all >95% except RS slope algorithm with 94.24% on normal ECG database and 94.44% on arrhythmia database). Thirdly, for the paced rhythm ECG database, all ten algorithms were immune to the paced beats (>94%) except the RS slope method, which only output a low F1 result of 78.99%. At last, the detection accuracies had obvious decreases when dealing with the dynamic telehealth ECG signals (all <80%) except OKB algorithm with 80.43%. Furthermore, the time costs from analyzing a 10 s ECG segment were given as the quantitative index of the computational complexity. All ten algorithms had high numerical efficiency (all <4 ms) except RS slope (94.07 ms) and sixth power algorithms (8.25 ms). And OKB algorithm had the highest numerical efficiency (1.54 ms).

ECG fiducial point extraction using switching Kalman filter

In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called "switch" is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and compared among two consecutive modes and a path is estimated, which shows the relation of each part of the ECG signal to the mode with the maximum probability. ECG FPs are found from the estimated path. For performance evaluation, the Physionet QT database is used and the proposed method is compared with methods based on wavelet transform, partially collapsed Gibbs sampler (PCGS) and extended Kalman filter. For our proposed method, the mean error and the root mean square error across all FPs are 2 ms (i.e. less than one sample) and 14 ms, respectively. These errors are significantly smaller than those obtained using other methods. The proposed method achieves lesser RMSE and smaller variability with respect to others.

A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm

BACKGROUND AND OBJECTIVES

Detection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals.

METHODS

A lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals.

RESULTS

The proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation.

CONCLUSIONS

In comparison with other related studies, the proposed algorithm offers a lightweight adaptive alternative for R-peak detection with good computational efficiency. The empirical results indicate that it not only yields a high accuracy rate in QRS detection, but also exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal.

A Combined Independent Source Separation and Quality Index Optimization Method for Fetal ECG Extraction from Abdominal Maternal Leads

The non-invasive fetal electrocardiogram (fECG) technique has recently received considerable interest in monitoring fetal health. The aim of our paper is to propose a novel fECG algorithm based on the combination of the criteria of independent source separation and of a quality index optimization (ICAQIO-based). The algorithm was compared with two methods applying the two different criteria independently—the ICA-based and the QIO-based methods—which were previously developed by our group. All three methods were tested on the recently implemented Fetal ECG Synthetic Database (FECGSYNDB). Moreover, the performance of the algorithm was tested on real data from the PhysioNet fetal ECG Challenge 2013 Database. The proposed combined method outperformed the other two algorithms on the FECGSYNDB (ICAQIO-based: 98.78%, QIO-based: 97.77%, ICA-based: 97.61%). Significant differences were obtained in particular in the conditions when uterine contractions and maternal and fetal ectopic beats occurred. On the real data, all three methods obtained very high performances, with the QIO-based method proving slightly better than the other two (ICAQIO-based: 99.38%, QIO-based: 99.76%, ICA-based: 99.37%). The findings from this study suggest that the proposed method could potentially be applied as a novel algorithm for accurate extraction of fECG, especially in critical recording conditions.

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

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

Non-invasive Fetal ECG Signal Quality Assessment for Multichannel Heart Rate Estimation

OBJECTIVE

The noninvasive fetal ECG (NI-FECG) from abdominal recordings offers novel prospects for prenatal monitoring. However, NI-FECG signals are corrupted by various nonstationary noise sources, making the processing of abdominal recordings a challenging task. In this paper, we present an online approach that dynamically assess the quality of NI-FECG to improve fetal heart rate (FHR) estimation.

METHODS

Using a naive Bayes classifier, state-of-the-art and novel signal quality indices (SQIs), and an existing adaptive Kalman filter, FHR estimation was improved. For the purpose of training and validating the proposed methods, a large annotated private clinical dataset was used.

RESULTS

The suggested classification scheme demonstrated an accuracy of Krippendorff's alpha in determining the overall quality of NI-FECG signals. The proposed Kalman filter outperformed alternative methods for FHR estimation achieving accuracy.

CONCLUSION

The proposed algorithm was able to reliably reflect changes of signal quality and can be used in improving FHR estimation.

SIGNIFICANCE

NI-ECG signal quality estimation and multichannel information fusion are largely unexplored topics. Based on previous works, multichannel FHR estimation is a field that could strongly benefit from such methods. The developed SQI algorithms as well as resulting classifier were made available under a GNU GPL open-source license and contributed to the FECGSYN toolbox.

A PCA/ICA based Fetal ECG Extraction from Mother Abdominal Recordings by Means of a Novel Data-driven Approach to Fetal ECG Quality Assessment

BACKGROUND

Fetal electrocardiography is a developing field that provides valuable information on the fetal health during pregnancy. By early diagnosis and treatment of fetal heart problems, more survival chance is given to the infant.

OBJECTIVE

MATERIALS AND METHODS

Here, we extract fetal ECG from maternal abdominal recordings and detect R-peaks in order to recognize fetal heart rate. On the next step, we find a better and more qualified extracted fetal ECG by using a novel approach.

RESULTS

In this paper, a PCA/ICA-based algorithm is proposed for extracting fetal ECG, and fetal R-peaks are detected as well. The method validates the quality of extracted ECGs and selects the best candidate fetal ECG to provide the required morphological ECG features such as fetal heart rate and RR interval for more clinical examinations. The method was evaluated using the dataset which was provided by PhysioNet/Computing in Cardiology Challenge 2013. The dataset consists of 75 recordings of 4-channel ECGs each containing 1-minute length for training and 100 similar recordings for testing.

CONCLUSION

When the proposed algorithm was applied to the test set, the scores of 85.853 bpm2 for fetal heart rate and an error of 9.725 ms RMS for fetal RR-interval estimation were obtained.