Deep learning applied to non-invasive ECG pregnancy monitoring: fetal ECG recovery and QRS complex detection

• Julia C. Remus UFRGS
• Thiago L. T. da Silveira UFRGS / UFSM

DOI: https://doi.org/10.5753/sbcas_estendido.2025.6905

Resumo

Non-invasive electrocardiography (niECG) is an option to mitigate the limitations of the current pregnancy monitoring methods. Still, one of the main challenges in its use is the extraction of the fetal signal (fECG) from the measured abdominal signal (aECG) due to the low signal-to-noise ratio and the overlap of the maternal and fetal QRS complexes in time and frequency domains. We present two encoder-decoder deep learning models that use regions of interest (RoI) to perform simultaneous fetal QRS complex (fQRS) detection and fECG recovery. We show that such a RoI-based end-to-end approach leads to robust state-of-the-art results in inter-subject and cross-dataset evaluations.

Referências

Behar, J. et al. (2016). A practical guide to non-invasive foetal electrocardiogram extraction and analysis. Physiological Measurement, 37(5):R1–R35.

Chaurasia, A. and Culurciello, E. (2017). Linknet: Exploiting encoder representations for efficient semantic segmentation. In IEEE Visual Communications and Image Processing, pages 1–4.

Chivers, S. C. et al. (2022). Measurement of the cardiac time intervals of the fetal ECG utilising a computerised algorithm: A retrospective observational study. JRSM Cardiovasc. Dis., 11:20480040221096209.

Ghonchi, H. and Abolghasemi, V. (2022). A dual attention-based autoencoder model for fetal ECG extraction from abdominal signals. IEEE Sensors Journal, 22(23):22908–22918.

Kahankova, R. et al. (2020). A review of signal processing techniques for non-invasive fetal electrocardiography. IEEE Reviews in Biomedical Engineering, 13:51–73.

Matonia, A. et al. (2020). Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations. Scientific Data, 7(1).

Mendis, L. et al. (2023). Computerised cardiotocography analysis for the automated detection of fetal compromise during labour: A review. Bioengineering, 10(9):1007.

Mohebbian, M. R. et al. (2022). Fetal ECG extraction from maternal ECG using attention-based CycleGAN. IEEE Journal of Biomedical and Health Informatics, 26(2):515–526.

Pan, J. and Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering, BME-32(3):230–236.

Rahman, A. et al. (2023). Fetal ECG extraction from maternal ECG using deeply supervised LinkNet++ model. Engineering Applications of Artificial Intelligence, 123:106414.

Rahman, M. M. et al. (2023). A systematic survey of data augmentation of ECG signals for AI applications. Sensors, 23(11):5237.

Remus, J. C. and da Silveira, T. L. T. (2024). An end-to-end RoI-based encoder-decoder for fetal ECG recovery and QRS complex detection. IEEE International Symposium on Medical Measurements and Applications, pages 1–6.

Sober, M. M. and Marco, J. G. (2007). Non-invasive fetal electrocardiogram database. Available at [link].

Vullings, R. (2010). Non-invasive fetal electrocardiogram:analysis and interpretation. PhD thesis, Technical University of Eindhoven.

Wang, X. et al. (2023). Correlation-aware attention CycleGAN for accurate fetal ECG extraction. IEEE Transactions on Instrumentation and Measurement, 72:1–13.

Zhong, W. et al. (2019). Fetal electrocardiography extraction with residual convolutional encoder–decoder networks. Australasian Physical; Engineering Sciences in Medicine, 42(4):1081–1089.