Fetal ECG extraction exploiting joint sparse supports in a dual dictionary framework
TL;DR: Simulation results with real ECG recordings demonstrate that the proposed scheme provides a comprehensive framework for eliminating the mother's ECG component in the abdominal recordings, effectively filters out noise and distortions, and leads to more accurate recovery of the fetal ECG source signal compared to other state-of-the-art algorithms.
About: This article is published in Biomedical Signal Processing and Control.The article was published on 2019-02-01. It has received 12 citations till now.
Citations
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TL;DR: This review highlights key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources and the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance.
Abstract: 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.
66 citations
TL;DR: The proposed method is promising for FECG extraction since it relies on filtering and wavelet analysis of two abdominal signals for the algorithm, and is easily adjusted based on the power levels of signals, giving it great ease of adaptation to changing signals in different biosignals applications.
Abstract: Objective Monitoring the heartbeat of the fetus during pregnancy is a vital part in determining their health. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The demand for a reliable method of non-invasive fetal heart monitoring is of high importance. Method Electrocardiogram (ECG) is a method of monitoring the electrical activity produced by the heart. The extraction of the fetal ECG (FECG) from the abdominal ECG (AECG) is challenging since both ECGs of the mother and the baby share similar frequency components, adding to the fact that the signals are corrupted by white noise. This paper presents a method of FECG extraction by eliminating all other signals using AECG. The algorithm is based on attenuating the maternal ECG (MECG) by filtering and wavelet analysis to find the locations of the FECG, and thus isolating them based on their locations. Two signals of AECG collected at different locations on the abdomens are used. The ECG data used contains MECG of a power of five to ten times that of the FECG. Results The FECG signals were successfully isolated from the AECG using the proposed method through which the QRS complex of the heartbeat was conserved, and heart rate was calculated. The fetal heart rate was 135 bpm and the instantaneous heart rate was 131.58 bpm. The heart rate of the mother was at 90 bpm with an instantaneous heart rate of 81.9 bpm. Conclusion The proposed method is promising for FECG extraction since it relies on filtering and wavelet analysis of two abdominal signals for the algorithm. The method implemented is easily adjusted based on the power levels of signals, giving it great ease of adaptation to changing signals in different biosignals applications.
30 citations
TL;DR: In this paper , a joint time-frequency analysis for extracting the fetal ECG using single-channel abdominal ECG is proposed, where the single maternal beat is constructed as a Maximum likelihood estimator, and then the abdomen ECG free from the maternal component is processed using S-transform to identify the fetal peaks.
10 citations
TL;DR: In this paper, a novel algorithm based on time-frequency analysis combining fractional Fourier transform (FrFT) and wavelet analysis was proposed to extract fetal ECG from abdominal signals at higher accuracy.
8 citations
6 citations
References
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TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Abstract: —The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of He...
11,407 citations
TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.
Abstract: In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and include compression, regularization in inverse problems, feature extraction, and more. Recent activity in this field has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. Designing dictionaries to better fit the above model can be done by either selecting one from a prespecified set of linear transforms or adapting the dictionary to a set of training signals. Both of these techniques have been considered, but this topic is largely still open. In this paper we propose a novel algorithm for adapting dictionaries in order to achieve sparse signal representations. Given a set of training signals, we seek the dictionary that leads to the best representation for each member in this set, under strict sparsity constraints. We present a new method-the K-SVD algorithm-generalizing the K-means clustering process. K-SVD is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data. The update of the dictionary columns is combined with an update of the sparse representations, thereby accelerating convergence. The K-SVD algorithm is flexible and can work with any pursuit method (e.g., basis pursuit, FOCUSS, or matching pursuit). We analyze this algorithm and demonstrate its results both on synthetic tests and in applications on real image data
8,905 citations
TL;DR: In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.
Abstract: The wavelet transform has emerged over recent years as a powerful time-frequency analysis and signal coding tool favoured for the interrogation of complex nonstationary signals. Its application to biosignal processing has been at the forefront of these developments where it has been found particularly useful in the study of these, often problematic, signals: none more so than the ECG. In this review, the emerging role of the wavelet transform in the interrogation of the ECG is discussed in detail, where both the continuous and the discrete transform are considered in turn.
794 citations
"Fetal ECG extraction exploiting joi..." refers methods in this paper
...Numerous approaches have been proposed in the literature to address this problem, such as those based on blind source separation (BSS) [5], [7], [8], [9], [10], [11], independent component analysis (ICA) [6], [12], [13], [14], [15], adaptive filtering [16], [17], sparse redundant representations [18], [19], and Wavelets [7], [20], [21], [22], [23]....
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TL;DR: The emerging technique of independent component analysis, also known as blind source separation, is proposed as an interesting tool for the extraction of the antepartum fetal electrocardiogram from multilead cutaneous potential recordings.
Abstract: We propose the emerging technique of independent component analysis, also known as blind source separation, as an interesting tool for the extraction of the antepartum fetal electrocardiogram from multilead cutaneous potential recordings. The technique is illustrated by means of a real-life example.
487 citations
"Fetal ECG extraction exploiting joi..." refers methods in this paper
...In [15], independent component analysis (ICA) based blind source subspace separation is proposed as a tool for the extraction of the antepartum fetal electrocardiogram from multilead cutaneous potential recordings....
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...Numerous approaches have been proposed in the literature to address this problem, such as those based on blind source separation (BSS) [5], [7], [8], [9], [10], [11], independent component analysis (ICA) [6], [12], [13], [14], [15], adaptive filtering [16], [17], sparse redundant representations [18], [19], and Wavelets [7], [20], [21], [22], [23]....
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TL;DR: A BSS procedure based on higher-order statistics and Widrow's multireference adaptive noise cancelling approach is compared and the experimental outcomes demonstrate the more robust performance of the blind technique and verify the validity of the BSS model in this important biomedical application.
Abstract: The problem of the fetal electrocardiogram (FECG) extraction from maternal skin electrode measurements can be modeled from the perspective of blind source separation (BSS). Since no comparison between BSS techniques and other signal processing methods has been made, the authors compare a BSS procedure based on higher-order statistics and Widrow's multireference adaptive noise cancelling approach. As a best-case scenario for this latter method, optimal Wiener-Hopf solutions are considered. Both procedures are applied to real multichannel ECG recordings obtained from a pregnant woman. The experimental outcomes demonstrate the more robust performance of the blind technique and, in turn, verify the validity of the BSS model in this important biomedical application.
339 citations
"Fetal ECG extraction exploiting joi..." refers methods in this paper
...In [10], a blind source separation technique is applied requiring at least three abdominal signals to separate each into MECG, FECG, and noise components....
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...we also applied the BSS technique presented in [10], adaptive filtering technique presented in [16], and the K-SVD based denoising technique presented in [19] to the same datasets....
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...Numerous approaches have been proposed in the literature to address this problem, such as those based on blind source separation (BSS) [5], [7], [8], [9], [10], [11], independent component analysis (ICA) [6], [12], [13], [14], [15], adaptive filtering [16], [17], sparse redundant representations [18], [19], and Wavelets [7], [20], [21], [22], [23]....
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