This paper introduces a novel algorithm for effective and accurate extraction of non-invasive fetal electrocardiogram (NI-fECG). In NI-fECG based monitoring, the useful signal is measured along with other signals generated by the pregnant women's body, especially maternal electrocardiogram (mECG). These signals are more distinct in magnitude and overlap in time and frequency domains, making the fECG extraction extremely challenging. The proposed extraction method combines the Grey wolf algorithm (GWO) with sequential analysis (SA). This innovative combination, forming the GWO-SA method, optimises the parameters required to create a template that matches the mECG, which leads to an accurate elimination of the said signal from the input composite signal. The extraction system was tested on two databases consisting of real signals, namely, Labour and Pregnancy. The databases used to test the algorithms are available on a server at the generalist repositories (figshare) integrated with Matonia et al. (Sci Data 7(1):1-14, 2020). The results show that the proposed method extracts the fetal ECG signal with an outstanding efficacy. The efficacy of the results was evaluated based on accurate detection of the fQRS complexes. The parameters used to evaluate are as follows: accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1 score. Due to the stochastic nature of the GWO algorithm, ten individual runs were performed for each record in the two databases to assure stability as well as repeatability. Using these parameters, for the Labour dataset, we achieved an average ACC of 94.60%, F1 of 96.82%, SE of 97.49%, and PPV of 98.96%. For the Pregnancy database, we achieved an average ACC of 95.66%, F1 of 97.44%, SE of 98.07%, and PPV of 97.44%. The obtained results show that the fHR related parameters were determined accurately for most of the records, outperforming the other state-of-the-art approaches. The poorer quality of certain signals have caused deviation from the estimated fHR for certain records in the databases. The proposed algorithm is compared with certain well established algorithms, and has proven to be accurate in its fECG extractions.

Centre for Artificial Intelligence Research and Optimisation Torrens University Australia 90 Bowen Terrace Brisbane QLD 4006 Australia

Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava 17 listopadu Ostrava 708 00 Czechia

Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava 17 listopadu Ostrava 708 00 Czechia

See more in PubMed

Kahankova R, Martinek R, Jaros R, Behbehani K, Matonia A, Jezewski M, Behar JA. A review of signal processing techniques for non-invasive fetal electrocardiography. IEEE reviews in biomedical engineering. 2019;13:51–73. doi: 10.1109/RBME.2019.2938061. PubMed DOI

Jezewski J, Wrobel J, Matonia A, Horoba K, Martinek R, Kupka T, Jezewski M. Is abdominal fetal electrocardiography an alternative to doppler ultrasound for fhr variability evaluation? Frontiers in physiology. 2017;8:305. doi: 10.3389/fphys.2017.00305. PubMed DOI PMC

Clifford GD, Silva I, Behar J, Moody GB. Non-invasive fetal ecg analysis. Physiological measurement. 2014;35(8):1521. PubMed PMC

Andreotti F, Riedl M, Himmelsbach T, Wedekind D, Wessel N, Stepan H, Schmieder C, Jank A, Malberg H, Zaunseder S. Robust fetal ecg extraction and detection from abdominal leads. Physiological measurement. 2014;35(8):1551. doi: 10.1088/0967-3334/35/8/1551. PubMed DOI

Silva, I., Behar, J., Sameni, R., Zhu, T., Oster, J., Clifford, G.D., Moody, G.B.: Noninvasive fetal ecg: the physionet/computing in cardiology challenge 2013. In: Computing in Cardiology 2013, pp. 149–152 (2013). IEEE PubMed PMC

Barnova K, Martinek R, Jaros R, Kahankova R, Behbehani K, Snasel V. System for adaptive extraction of non-invasive fetal electrocardiogram. Applied Soft Computing. 2021;113:107940. doi: 10.1016/j.asoc.2021.107940. DOI

Jaros R, Martinek R, Kahankova R, Koziorek J. Novel hybrid extraction systems for fetal heart rate variability monitoring based on non-invasive fetal electrocardiogram. IEEE Access. 2019;7:131758–131784. doi: 10.1109/ACCESS.2019.2933717. DOI

Kahankova R, Mikolasova M, Martinek R. Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction. PloS one. 2022;17(4):0266807. doi: 10.1371/journal.pone.0266807. PubMed DOI PMC

De Lathauwer L, De Moor B, Vandewalle J. Fetal electrocardiogram extraction by blind source subspace separation. IEEE transactions on biomedical engineering. 2000;47(5):567–572. doi: 10.1109/10.841326. PubMed DOI

Callaerts, D.: Signal separation methods based on singular value decomposition and their application to the real-time extraction of the fetal electrocardiogram from cutaneous recordings (1989)

Martinek R, Kahankova R, Jezewski J, Jaros R, Mohylova J, Fajkus M, Nedoma J, Janku P, Nazeran H. Comparative effectiveness of ica and pca in extraction of fetal ecg from abdominal signals: Toward non-invasive fetal monitoring. Frontiers in physiology. 2018;9:648. doi: 10.3389/fphys.2018.00648. PubMed DOI PMC

Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95-international Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995). IEEE

Karaboga, D., et al.: An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes university, engineering faculty, computer

Rajabioun R. Cuckoo optimization algorithm. Applied soft computing. 2011;11(8):5508–5518. doi: 10.1016/j.asoc.2011.05.008. DOI

Yang X-S. Firefly algorithm, stochastic test functions and design optimisation. International journal of bio-inspired computation. 2010;2(2):78–84. doi: 10.1504/IJBIC.2010.032124. DOI

Shiqin, Y., Jianjun, J., Guangxing, Y.: A dolphin partner optimization. In: 2009 WRI Global Congress on Intelligent Systems, vol. 1, pp. 124–128 (2009). IEEE

Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Advances in engineering software. 2014;69:46–61. doi: 10.1016/j.advengsoft.2013.12.007. DOI

Matonia A, Jezewski J, Kupka T, Jezewski M, Horoba K, Wrobel J, Czabanski R, Kahankowa R. Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations. Scientific data. 2020;7(1):1–14. doi: 10.1038/s41597-020-0538-z. PubMed DOI PMC

Muro C, Escobedo R, Spector L, Coppinger R. Wolf-pack (canis lupus) hunting strategies emerge from simple rules in computational simulations. Behavioural processes. 2011;88(3):192–197. doi: 10.1016/j.beproc.2011.09.006. PubMed DOI

Martens SM, Rabotti C, Mischi M, Sluijter RJ. A robust fetal ecg detection method for abdominal recordings. Physiological measurement. 2007;28(4):373. doi: 10.1088/0967-3334/28/4/004. PubMed DOI

Ghobadi Azbari P, Abdolghaffar M, Mohaqeqi S, Pooyan M, Ahmadian A, Ghanbarzadeh Gashti N. A novel approach to the extraction of fetal electrocardiogram based on empirical mode decomposition and correlation analysis. Australasian Physical & Engineering Sciences in Medicine. 2017;40(3):565–574. doi: 10.1007/s13246-017-0560-4. PubMed DOI

Zhang N, Zhang J, Li H, Mumini O, Samuel O, Ivanov K, Wang L. A Novel Technique for Fetal ECG Extraction Using Single-Channel Abdominal Recording. Sensors. 2017;17(3):457. doi: 10.3390/s17030457. PubMed DOI PMC

Billeci L, Varanini M. A combined independent source separation and quality index optimization method for fetal ecg extraction from abdominal maternal leads. Sensors. 2017;17(5):1135. doi: 10.3390/s17051135. PubMed DOI PMC

Matonia A, Jezewski J, Kupka T, Jezewski M, Horoba K, Wrobel J, Czabanski R, Kahankowa R. Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations. Scientific Data. 2020;7(1):200. doi: 10.1038/s41597-020-0538-z. PubMed DOI PMC

Robinson B. A review of nichd standardized nomenclature for cardiotocography: the importance of speaking a common language when describing electronic fetal monitoring. Reviews in Obstetrics and Gynecology. 2008;1(2):56. PubMed PMC

Barnova K, Martinek R, Jaros R, Kahankova R, Matonia A, Jezewski M, Czabanski R, Horoba K, Jezewski J. A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ecg extraction. PloS one. 2021;16(8):0256154. doi: 10.1371/journal.pone.0256154. PubMed DOI PMC

Andreotti F, Gräßer F, Malberg H, Zaunseder S. Non-invasive fetal ecg signal quality assessment for multichannel heart rate estimation. IEEE Transactions on Biomedical Engineering. 2017;64(12):2793–2802. doi: 10.1109/TBME.2017.2675543. PubMed DOI

Ortiz A, Mendez E, Balderas D, Ponce P, Macias I, Molina A. Hardware implementation of metaheuristics through labview fpga. Applied Soft Computing. 2021;113:107908. doi: 10.1016/j.asoc.2021.107908. DOI

Zhao Z, Zhang Y. Sqi quality evaluation mechanism of single-lead ecg signal based on simple heuristic fusion and fuzzy comprehensive evaluation. Frontiers in physiology. 2018;9:727. doi: 10.3389/fphys.2018.00727. PubMed DOI PMC

Zhang Y, Yu S. Single-lead noninvasive fetal ECG extraction by means of combining clustering and principal components analysis. Medical & Biological Engineering & Computing. 2020;58(2):419–432. doi: 10.1007/s11517-019-02087-7. PubMed DOI

Zhou Z, Huang K, Qiu Y, Shen H, Ming Z. Morphology extraction of fetal electrocardiogram by slow-fast LSTM network. Biomedical Signal Processing and Control. 2021;68:102664. doi: 10.1016/j.bspc.2021.102664. DOI

Andreotti F, Behar J, Zaunseder S, Oster J, Clifford GD. An open-source framework for stress-testing non-invasive foetal ECG extraction algorithms. Physiological Measurement. 2016;37(5):627–648. doi: 10.1088/0967-3334/37/5/627. PubMed DOI

Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23) (2000). 10.1161/01.CIR.101.23.e215 PubMed

Clifford GD, Silva I, Behar J, Moody GB. Non-invasive fetal ECG analysis. Physiological Measurement. 2014;35(8):1521–1536. doi: 10.1088/0967-3334/35/8/1521. PubMed DOI PMC

Jezewski, J., Matonia, A., Kupka, T., Roj, D., Czabanski, R.: Determination of fetal heart rate from abdominal signals: Evaluation of beat-to-beat accuracy in relation to the direct fetal electrocardiogram. Biomedizinische Technik/Biomedical Engineering 57(5) (2012). 10.1515/bmt-2011-0130 PubMed

Castillo E, Morales DP, García A, Parrilla L, Ruiz VU, Álvarez-Bermejo JA. A clustering-based method for single-channel fetal heart rate monitoring. PLoS One. 2018;13(6):0199308. doi: 10.1371/journal.pone.0199308. PubMed DOI PMC

Zhong W, Liao L, Guo X, Wang G. Fetal electrocardiography extraction with residual convolutional encoder-decoder networks. Australasian physical & engineering sciences in medicine. 2019;42(4):1081–1089. doi: 10.1007/s13246-019-00805-x. PubMed DOI

Gurve D, Krishnan S. Separation of Fetal-ECG From Single-Channel Abdominal ECG Using Activation Scaled Non-Negative Matrix Factorization. IEEE Journal of Biomedical and Health Informatics. 2020;24(3):669–680. doi: 10.1109/JBHI.2019.2920356. PubMed DOI

Taha L, Abdel-Raheem E. A Null Space-Based Blind Source Separation for Fetal Electrocardiogram Signals. Sensors. 2020;20(12):3536. doi: 10.3390/s20123536. PubMed DOI PMC

Jaba Deva Krupa, A., Dhanalakshmi, S., Sanjana, N.L., Manivannan, N., Kumar, R., Tripathy, S.: Fetal heart rate estimation using fractional Fourier transform and wavelet analysis. Biocybernetics and Biomedical Engineering 41(4), 1533–1547 (2021). 10.1016/j.bbe.2021.09.006

Jallouli M, Arfaoui S, Ben Mabrouk A, Cattani C. Clifford Wavelet Entropy for Fetal ECG Extraction. Entropy. 2021;23(7):844. doi: 10.3390/e23070844. PubMed DOI PMC

Abel, J.D.K., Dhanalakshmi, S., Kumar, R.: A comprehensive survey on signal processing and machine learning techniques for non-invasive fetal ecg extraction. Multimedia Tools and Applications, 1–28 (2022)

Lee KJ, Lee B. End-to-end deep learning architecture for separating maternal and fetal ecgs using w-net. IEEE Access. 2022;10:39782–39788. doi: 10.1109/ACCESS.2022.3166925. DOI

Fotiadou E, Vullings R. Multi-channel fetal ecg denoising with deep convolutional neural networks. Frontiers in Pediatrics. 2020;8:508. doi: 10.3389/fped.2020.00508. PubMed DOI PMC

Frank, A.: UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. http://archive. ics. uci. edu/ml (2010)

Chudacek V, Spilka J, Bursa M, Janku P, Hruban L, Huptych M, Lhotská L. Open access intrapartum CTG database. BMC Pregnancy and Childbirth. 2014;14(1):16. doi: 10.1186/1471-2393-14-16. PubMed DOI PMC

See more in PubMed

figshare
10.6084/m9.figshare.c.474079418