Fetal ECG extraction from a single abdominal ECG signal using SVD and polynomial classifiers

Ayat, Mohammad; Assaleh, Khaled; Al‐Nashash, Hasan

doi:10.1109/mlsp.2008.4685488

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…Recent studies [31,32,[37][38][39][40][41][42][43][44][45] show that the use of a single algorithm is not as promising as a combination of multiple algorithms. The research efforts are, therefore, aimed at testing various combinations of two or more algorithms that would overcome the limitations of individual methods while combining their advantages.…”

Section: State-of-the-art Methods For Fecg Signal Extractionmentioning

confidence: 99%

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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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Section: State-of-the-art Methods For Fecg Signal Extractionmentioning

confidence: 99%

“…A combination of singular value decomposition and polynomial classifiers was introduced in [38]. The mECG signal was estimated using the singular value decomposition and subsequently mECG and aECG signals were fed to the input of the polynomial classifiers.…”

Section: Single Channel Methodsmentioning

confidence: 99%

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

et al. 2021

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“…Ayat et al [ 111 ] used a combination of SVD and polynomial classifiers (PC) for fECG extraction. By using SVD, mECG, which is used with aECG as an input for PC that extract fECG by exploiting the dynamics and the nonlinearities of the mECG, is estimated.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Non-Adaptive Methods for Fetal ECG Signal Processing: A Review and Appraisal

Jaroš

Martínek

Kahánková

2018

Sensors

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Fetal electrocardiography is among the most promising methods of modern electronic fetal monitoring. However, before they can be fully deployed in the clinical practice as a gold standard, the challenges associated with the signal quality must be solved. During the last two decades, a great amount of articles dealing with improving the quality of the fetal electrocardiogram signal acquired from the abdominal recordings have been introduced. This article aims to present an extensive literature survey of different non-adaptive signal processing methods applied for fetal electrocardiogram extraction and enhancement. It is limiting that a different non-adaptive method works well for each type of signal, but independent component analysis, principal component analysis and wavelet transforms are the most commonly published methods of signal processing and have good accuracy and speed of algorithms.

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“…Some signal processing methods have been used in FECG extraction, such as the adaptive filter [2,[7][8][9], event synchronous canceller (ESC) [10][11][12], singular value decomposition (SVD) [13,14], blind source separation (BSS) [15][16][17] and artificial neural network (ANN) [3,4,18]. Whereas BSS requires multiple leads to collect several electrocardiogram (ECG) signals [19].…”

Section: Introductionmentioning

confidence: 99%

Method of fetal electrocardiogram extraction based on ν ‐support vector regression

Han

Chen

2015

IET signal process.

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A new method based on v-support vector regression (v-SVR) is proposed to extract the fetal electrocardiogram (FECG) from the abdominal signal recorded at the abdominal areas of the pregnant woman. The maternal electrocardiogram (MECG) component in the abdominal signal is a non-linearly transformed version of the MECG and the non-linear transform is estimated by v-SVR. The optimal estimation of the MECG component is obtained by the MECG undergoing the estimated non-linear transform. Then the FECG is extracted by subtracting the estimated MECG component from the abdominal signal. The method is validated by the experiments on both synthetic and real electrocardiogram (ECG) signals. The visual results and signal-to-noise ratio (SNR) are used to evaluate the performance of the FECG extraction methods. The experimental results indicated that the proposed method can be used for extracting the FECG from the abdominal signal.

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Fetal ECG extraction from a single abdominal ECG signal using SVD and polynomial classifiers

Cited by 9 publications

References 17 publications

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

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

Non-Adaptive Methods for Fetal ECG Signal Processing: A Review and Appraisal

Method of fetal electrocardiogram extraction based on ν ‐support vector regression

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