Separation of Fetal-ECG From Single-Channel Abdominal ECG Using Activation Scaled Non-Negative Matrix Factorization

Gurve, Dharmendra; Krishnan, Sridhar

doi:10.1109/jbhi.2019.2920356

Cited by 48 publications

(17 citation statements)

References 37 publications

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“…A few exploratory studies on other BSS methods, such as singular value decomposition and non-negative matrix factorization were also reported in literature, but a large consensus is reached on the use of ICA [22]. Indeed, ICA exploits the assumptions that the source components are non-Gaussian and statistically independent, enabling us to obtain accurate fHR estimations with low sensitivity to noise and artifacts [23]. For this reason, ICA was our choice in [18].…”

Section: This Approach Considers a Set Of N Individual Componentsmentioning

confidence: 99%

Automatic Optimization of Multichannel Electrode Configurations for Robust Fetal Heart Rate Detection by Blind Source Separation

Galli

Peri

Rabotti

et al. 2023

IEEE Trans. Biomed. Eng.

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Objective. Fetal heart rate (fHR) evaluation is fundamental to guarantee timely medical intervention in case of pregnancy complications. Due to the limitations of traditional cardiotocography, multichannel electrophysiological recording was proposed as a viable alternative, which requires Blind Source Separation (BSS) techniques. Yet effective and reliable separation of the fetal ECG remains challenging due to multiple noise sources and the effects of varying fetal position. In this work, we demonstrate that the adopted electrode configuration plays a key role in the effectiveness of BSS and propose guidelines for optimal electrode positioning. Moreover, a model is proposed to automatically predict the most suited configuration for accurate BSS-based fHR estimation with a minimal number of leads, to facilitate practical implementation. Methods. We compared fHR estimation accuracy with different electrode configurations on in-silico data, identifying the optimal configuration for a recent BSS method. Based on features extracted from raw signals, we proposed a support vector regression model to automatically identify the best electrode configuration in terms of fHR estimation accuracy and to dynamically adjust it to varying fetal presentation. Evaluation was performed on real and synthetic data. Results. Guidelines for the optimal electrode configuration are proposed by using 4 leads. Prediction of configuration quality shows 80.9% accuracy; the optimal configuration is recognized in 92.2% of the subjects. Conclusion. The proposed method successfully predicts the quality of the configurations, demonstrating the impact of the electrode configuration on the BSS performance. Significance. The method holds potential for long-term fetal monitoring, by dynamically choosing the optimal configuration.

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Section: This Approach Considers a Set Of N Individual Componentsmentioning

confidence: 99%

Automatic Optimization of Multichannel Electrode Configurations for Robust Fetal Heart Rate Detection by Blind Source Separation

Galli

Peri

Rabotti

et al. 2023

IEEE Trans. Biomed. Eng.

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“…• In [99], the authors introduced a combination of CS and non-negative matrix factorization (NMF). The method was tested on five recordings from the ADFECGDB database and 60 recordings from the Challenge 2013 database (including 25 records that we used for testing).…”

Section: Plos Onementioning

confidence: 99%

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

et al. 2021

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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).

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“…W ORLDWIDE, around 2.65 million babies are stillborn each year, and heart problems are the leading cause of death due to birth defects. According to the American Heart Association, in the United States, about 40,000 children are born with heart disease each year, and at least eight out of every 1,000 babies have a heart defect [1]. As a result, there is a need for methods to prevent and reduce fetal mortality during pregnancy.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, by finding the signal source matrix, BSS estimates the FECG signal, which is computationally very expensive. Other traditional models like independent component analysis (ICA) [6], [7], and non-negative matrix factorization (NMF) [8] have also been studied. However, these methods have convergence issues and are costly in terms of memory storage and resources, and are not very accurate.…”

Section: Introductionmentioning

confidence: 99%

A Dual Attention-Based Autoencoder Model for Fetal ECG Extraction From Abdominal Signals

Ghonchi

Abolghasemi

2022

IEEE Sensors J.

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Fetal electrocardiogram (FECG) signals contain important information about the conditions of the fetus during pregnancy. Currently, pure FECG signals can only be obtained through an invasive acquisition process which is life-threatening to both mother and fetus. In this study, single-channel ECG signals from the mother's abdomen are analysed with the aim of extracting the clean FECG waveform. This is a challenging task due to the very low amplitude of the FECG, various noises involved in the signal acquisition, and the overlap of R waves. To address this problem, we propose a novel convolutional auto-encoder network architecture to learn and extract the FECG patterns. The proposed model is equipped with a dual attention model, composed of squeeze-and-excitation and channel-wise modules, in the encoder and decoder blocks, respectively. It also benefits from a bidirectional long short-term memory (LSTM) layer. This combination allows the proposed network to accurately attend to and extract FECG signals from abdominal data. Three well-established datasets are considered in our experiments. The obtained results of FECG extraction are promising and confirm the effectiveness of using attention modules within the deep learning model. The results also suggest that the proposed auto-encoder network can accurately extract the fetal ECG signals where no information about maternal ECG is available.

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Separation of Fetal-ECG From Single-Channel Abdominal ECG Using Activation Scaled Non-Negative Matrix Factorization

Cited by 48 publications

References 37 publications

Automatic Optimization of Multichannel Electrode Configurations for Robust Fetal Heart Rate Detection by Blind Source Separation

Automatic Optimization of Multichannel Electrode Configurations for Robust Fetal Heart Rate Detection by Blind Source Separation

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

A Dual Attention-Based Autoencoder Model for Fetal ECG Extraction From Abdominal Signals

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