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

Sarafan, Sadaf; Le, Tai; Naderi, Amir Mohammad; Nguyen, Quoc-Dinh; Kuo, Brandon Tiang-Yu; Ghirmai, Tadesse; Han, Huy-Dung; Lau, Michael; Cao, Hung

doi:10.3390/technologies8020033

Cited by 27 publications

(21 citation statements)

References 34 publications

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“…This will be discussed in detail in later sections. Finally, the fetal QRS complex (fQRS) is detected using the Pan-Tompkin algorithm [32, 37].…”

Section: Theory and Methodsmentioning

confidence: 99%

“…This will be discussed in detail in later sections. Finally, the fetal QRS complex (fQRS) is detected using the Pan-Tompkin algorithm [32,37]. In each case, reference annotations marking the locations of each fetal QRS complex were produced, usually with reference to a direct fECG signal, acquired from a fetal scalp electrode.…”

Section: Enkf Based Fecg Extraction Algorithmmentioning

confidence: 99%

“…The online databank, however, was obtained in the clinical setting, where motion noise was mostly non-existent because the subjects were in a resting position. We added to the online databank realistic motion artifacts, whose generation process is described in our previous paper [32], to mimic real-life scenarios. In this experiment, the ECG data were recorded from a healthy subject during different types of activities.…”

Section: Modified Signals With Motional Artifacts Addedmentioning

confidence: 99%

“…Our group has been developing abdominal patch devices and systems for the acquisition and extraction of fECG [14,31,32]. As we aim to make the device compact and unobtrusive, a single recording channel is desired for saving space; consequently, fECG extraction is almost impossible, especially with the presence of motion noise.…”

Section: Introductionmentioning

confidence: 99%

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

Sarafan

Lau³

et al. 2021

Preprint

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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.

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“…This will be discussed in detail in later sections. Finally, the fetal QRS complex (fQRS) is detected using the Pan-Tompkin algorithm [32, 37].…”

Section: Theory and Methodsmentioning

confidence: 99%

Section: Enkf Based Fecg Extraction Algorithmmentioning

confidence: 99%

Section: Modified Signals With Motional Artifacts Addedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Sarafan

Lau³

et al. 2021

Preprint

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“…1. ICA-the ICA method is a separation technique able to decompose a composite input signal, such as aECG, into its components, such as fECG, mECG, and noise [46,47,72]. The advantage of the FastICA variant is that it requires little memory space, is conceptually simple and computationally efficient, as it uses a fixed iteration scheme [25,73].…”

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

Mansourian,

Sarafan,

Torkamani-Azar

et al. 2024

Phys Eng Sci Med

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The fetal electrocardiogram (fECG), extracted from the abdominal ECG (aECG) signal of an expectant mother, provides vital information about the fetal condition. There is an increased interest in continuous fECG monitoring during pregnancy to ensure the well-being and development of the fetus. The implementation of continuous fECG monitoring devices necessitates robust and efficient signal processing techniques for extracting the fECG from the aECG. However, existing fECG extraction algorithms are not suitable for wearables due to the requirement of multi-channel signals and/or their high computational cost. In this paper, we propose a novel algorithm for fetal QRS extraction from a singlechannel aECG signal, which is robust and computationally less complex. The algorithm is based on a novel Permutation Entropy (PE) method we developed and referred to as the Adaptive Improved Permutation Entropy (AIPE) method. To evaluate the performance of the proposed algorithm, we utilized our own clinical data obtained from a pilot study with 10 subjects, each recording lasting 20 i minutes, as well as data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology demonstrates an average positive predictive value (PPV) of 91.0227%, sensitivity (SE) of 90.4726%, and F1-score of 90.6525% from the PhysioNet 2013 Challenge bank, outperforming other methods. These results indicate that our proposed algorithm is reliable and effective, making it suitable for deployment in wearable devices for continuous home-based monitoring of unborn babies.

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

Cited by 27 publications

References 34 publications

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

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

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

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

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