Fotiadou E, Sloun R, Laar J et al (2021) A dilated inception CNN-LSTM network for fetal heart rate estimation. Physiol Meas 42(4):045007

Article  Google Scholar 

Clifford GD, Silva I, Behar J et al (2014) Non-invasive fetal ECG analysis. Physiol Meas 35(8):1521

Article  PubMed  PubMed Central  Google Scholar 

Shaw CJ, Lees CC, Giussani DA (2016) Variations on fetal heart rate variability. J Physiol 594(5):1279

Article  CAS  PubMed  PubMed Central  Google Scholar 

Andreotti F, Grer F, Malberg H et al (2017) Non-Invasive Fetal ECG Signal Quality Assessment for Multichannel Heart Rate Estimation. IEEE Trans Biomed Eng 12:1–1

Google Scholar 

Zhou Z, Huang K, Qiu Y et al (2021) Morphology extraction of fetal electrocardiogram by slow-fast LSTM network. Biomed Signal Process Control 68:102664

Article  Google Scholar 

Kahankova R, Martinek R, Jaros R et al (2019) A review of signal processing techniques for non-invasive fetal electrocardiography. IEEE Rev Biomed Eng 13:51–73

Article  PubMed  Google Scholar 

Widrow B, Glover JR, McCool JM, Kaunitz J, Williams CS, Hearn RH, Zeidler JR, Dong JE, Goodlin RC (1975) Adaptive noise cancelling: Principles and applications. Proc IEEE 63(12):1692–1716

Article  Google Scholar 

Kanjilal PP, Palit S, Saha G (1997) Fetal ECG extraction from single-channel maternal ECG using singular value decomposition. IEEE Trans Biomed Eng 44(1):51–59

Article  CAS  PubMed  Google Scholar 

Bergveld P, Meijer WJ (1981) A new technique for the suppression of the MECG. IEEE Trans Biomed Eng 4:348–354

Article  Google Scholar 

Martens SM, Rabotti C, Mischi M, Sluijter RJ (2007) A robust fetal ECG detection method for abdominal recordings. Physiol Meas 28(4):373

Article  PubMed  Google Scholar 

Behar J, Andreotti F, Oster J, Clifford GD (2014) A Bayesian filtering framework for accurate extracting of the non-invasive FECG morphology. In: Computing in cardiology 2014, 2014. IEEE. pp. 53–56

Matonia A., Jezewski J., Horoba K., Gacek A., Labaj P (2006) The maternal ECG suppression algorithm for efficient extraction of the fetal ECG from abdominal signal. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp 3106–3109

Niknazar M, Rivet B, Jutten C (2013) Fetal ECG extraction by extended state Kalman filtering based on single-channel recordings. IEEE Trans Biomed Eng 60(5):1345–1352

Article  PubMed  Google Scholar 

Sarafan S et al (2022) A Novel ECG Denoising Scheme Using the Ensemble Kalman Filter. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, pp 2005–2008

Sutha P, Jayanthi VE (2018) Fetal electrocardiogram extraction and analysis using adaptive noise cancellation and wavelet transformation techniques. J Med Syst 42:1–18

Article  Google Scholar 

Mohammed Kaleem A, Kokate RD (2021) A survey on FECG extraction using neural network and adaptive filter. Soft Comput 25(6):4379–4392

Article  Google Scholar 

Xue J, Yu L (2021) Applications of machine learning in ambulatory ECG. Hearts 2(4):472–494

Article  Google Scholar 

Zhong W, Liao L, Guo X et al (2019) Fetal electrocardiography extraction with residual convolutional encoder–decoder networks. Australas Phys Eng Sci Med 42(4):1081–1089

Article  PubMed  Google Scholar 

Wei Xu, Wei Z, Wei Q et al (2020) Research and application of a deep learning model. Comput Technol Dev 30(7):5

Google Scholar 

Rasti-Meymandi A, Ghaffari A (2021) AECG-DecompNet: abdominal ECG signal decomposition through deep-learning model. Physiol Meas 42(4):045002

Article  Google Scholar 

Behar J, Andreotti F, Zaunseder S et al (2014) An ECG simulator for generating maternal-foetal activity mixtures on abdominal ECG recordings. Physiol Meas 35(8):1537

Article  PubMed  Google Scholar 

PhysioBank PT (2000) PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):215–220

Google Scholar 

Widrow B, Mccool JM Jr et al (1976) Adaptive Noise Cancelling: Principles and Applications. Proceedings of the IEEE 63(12):1692–1716

Article  Google Scholar 

Ifeachor EC et al (2004) Nonlinear methods for biopattern analysis: role and challenges. In: The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp 5400–5406

Ma Y, Xiao Y, Wei G et al (2018) Foetal ECG extraction using non-linear adaptive noise canceller with multiple primary channels. IET Signal Proc 12(2):219–227

Article  Google Scholar 

Qiu Y (2019) Research on fetal ECG extraction method based on recurrent neural network. Zhejiang University, Zhejiang

Google Scholar 

Wang X, Xiaoyang Z, Xinying W et al (2022) Super resolution reconstruction of single image based on non decimated wavelet edge learning depth residual network. J Electron 50(7):1753–1765

Google Scholar 

Zhang F, Cai N, Wu J et al (2018) Image denoising method based on a deep convolution neural network. IET Image Proc 12(4):485–493

Article  Google Scholar 

Hewage P, Trovati M, Pereira E et al (2021) Deep learning-based effective fine-grained weather forecasting model. Pattern Anal Appl 24(1):343–366

Article  Google Scholar 

Yuqing Y, Jianghui C, Haifeng Y et al (2022) LAMOST low-quality spectral analysis based on influence space and data field. Spectrosc Spectral Anal 42(4):1186–1191

Google Scholar 

Billeci L, Varanini M (2017) A combined independent source separation and quality index optimization method for fetal ECG extraction from abdominal maternal leads. Sensors 17(5):1135

Article  PubMed  PubMed Central  Google Scholar 

Behar J, Johnson A, Clifford GD et al (2014) A comparison of single channel fetal ECG extraction methods. Ann Biomed Eng 42(6):1340–1353

Article  PubMed  Google Scholar