Ulvi BAŞPINAR, Yasemin KÖYLÜ

DETECTION OF FETAL ELECTROCARDIOGRAM SIGNALS FROM MATERNAL ABDOMINAL ECG RECORDINGS

Fetal electrocardiogram (fECG) is a signal that contains vital information about the health of the fetus throughout pregnancy. During pregnancy, it is important to monitor and analyse this signal because it represents the electrical activity of the developing fetal heart. Early detection of fetal ECG problems during the fetus' development is crucial because it allows early treatment and provides knowledge about diseases that may emerge at a later time. Extraction of fetal ECG from the abdomen ECG signal is valuable in these aspects. In order to extract the fetal ECG from the recorded abdomen ECG signals correctly, it must be handled appropriately. It could be challenging to separate the fetal ECG signal from other physiological artifacts and noises in the mother abdominal signal. In this study, signal processing techniques were used to separate the fetus ECG signal from real abdominal ECG recordings. These methods include Ensemble Empirical Based Denoising, Finite Impulse Response Filter, Independent Component Analysis, and Pan & Tompkins approach. The results show that utilizing only the ICA technique to extract fECG signals is insufficient and that additional algorithms, such as those indicated above, should be used together. The mECG and fECG signals can be successfully extracted using the suggested approach.

Keywords:

fECG extraction mECG and fECG separation, Wearable fECG monitoring,

PDF

___

[1] Verdurmen, K.M., Eijsvoogel, N.B., Lempersz, C., Vullings, R., Schroer, C., van Laar, J.O., Oei, S.G. (2016). A systematic review of prenatal screening for congenital heart disease by fetal electrocardiography, International Journal of Gynecology & Obstetrics, 135 129-134.
[2] Yang, Z., Zhou, Q., Lei, L., Zheng, K., Xiang, W. (2016). An IoT-cloud based wearable ECG monitoring system for smart healthcare, Journal of medical systems, 40 1-11.
[3] Spanò, E., Di Pascoli, S., Iannaccone, G. (2016). Low-power wearable ECG monitoring system for multiple-patient remote monitoring, IEEE Sensors Journal, 16 5452-5462.
[4] Martinek, R., Kahankova, R., Nazeran, H., Konecny, J., Jezewski, J., Janku, P., Bilik, P., Zidek, J., Nedoma, J., Fajkus, M. (2017). Non-invasive fetal monitoring: A maternal surface ECG electrode placement-based novel approach for optimization of adaptive filter control parameters using the LMS and RLS algorithms, Sensors, 17 1154.
[5] Sameni, R., Clifford, G.D. (2010). A review of fetal ECG signal processing; issues and promising directions, The open pacing, electrophysiology & therapy journal, 3 4.
[6] Preethi, D., Valarmathi, R. (2018). An Analysis of FIR Filter Algorithms in Fetal Heart Rate Monitoring, 2018 International Conference On Advances in Communication and Computing Technology (ICACCT), IEEE, 265-268.
[7] Widrow, B., Glover, J.R., McCool, J.M., Kaunitz, J., Williams, C.S., Hearn, R.H., Zeidler, J.R., Dong, J.E., Goodlin, R.C. (1975). Adaptive noise cancelling: Principles and applications, Proceedings of the IEEE, 63 1692-1716.
[8] Kahankova, R., Martinek, R., Mikolášová, M., Jaroš, R. (2018). Adaptive linear neuron for fetal electrocardiogram extraction, IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), IEEE, 2018, pp. 1-5.
[9] Alkhodari, M., Rashed, A., Alex, M., Yeh, N.-S. (2018) Fetal ECG Extraction Using Independent Components and Characteristics Matching, International Conference on Signal Processing and Information Security (ICSPIS), IEEE, 1-4.
[10] Cardenas-Lattus, J., Kaschel, H. (2017). Fetal ECG multi-level analysis using daubechies wavelet transform for non-invasive maternal abdominal ECG recordings, 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), IEEE, 1-6.
[11] Gupta, P., Sharma, K.K., Joshi, S.D. (2016). Fetal heart rate extraction from abdominal electrocardiograms through multivariate empirical mode decomposition, Computers in biology and medicine, 68 121-136.
[12] Hasan, M.A., Reaz, M.B.I., Ibrahimy, M.I. (2011). Fetal electrocardiogram extraction and R-peak detection for fetal heart rate monitoring using artificial neural network and Correlation, The 2011 International Joint Conference on Neural Networks, IEEE, 15-20.
[13] Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals, circulation, 101 e215-e220.
[14] Taralunga, D.D., Gussi, I., Strungaru, R. (2016). A new method for fetal electrocardiogram denoising using blind source separation and empirical mode decomposition, Revue Roumaine des Sci Techn, serie Électrotechnique et Énergetique, 61 94-98.
[15] Nikam, S., Deosarkar, S. (2016) Fast ICA based technique for non-invasive fetal ECG extraction, 2016 Conference on Advances in Signal Processing (CASP), IEEE, 60-65.
[16] Hyvärinen, A., Oja, E. (2000). Independent component analysis: algorithms and applications, Neural networks, 13 411-430.
[17] Hassan, N., Ramli, D.A. (2018). A comparative study of blind source separation for bioacoustics sounds based on FastICA, PCA and NMF, Procedia Computer Science, 126 363-372.
[18] Roshini, M., Thanaraj, K.P. (2016). Extraction of Fetal QRS Complex from Abdominal ECG Signals, International Journal of Computer Trends and Technology (IJCTT), 34-1 29-34
[19] Taralunga, D.D., Gussi, I., Strungaru, R. (2016). A new method for fetal electrocardiogram denoising using blind source separation and empirical mode decomposition, Revue Roumaine des Sci. Techn., serie Électrotechnique et Énergetique, 61 94-98.
[20] Wei, Z., Xiaolong, L., Jin, Z., Xueyun, W., Hongxing, L. (2018). Foetal heart rate estimation by empirical mode decomposition and MUSIC spectrum, Biomedical Signal Processing and Control, 42 287-296.
[21] Jaros, R., Martinek, R., Kahankova, R. (2018). Non-adaptive methods for fetal ECG signal processing: a review and appraisal, Sensors, 18 3648.
[22] Yin, J., Chen, X., Zhang, P., Shao, L., Li, J., Liu, H. (2020). Research on ECG signal denoising by combination of EEMD and NLM, 2020 Chinese Control And Decision Conference (CCDC), IEEE, 5033-5038.
[23] Gong, Y., Wang, Z., Xu, G., Zhang, Z. (2018). A comparative study of groundwater level forecasting using data-driven models based on ensemble empirical mode decomposition, Water, 10 730.
[24] Pavlatos, C., Dimopoulos, A., Manis, G. (2005). Papakonstantinou, G., Hardware implementation of Pan & Tompkins QRS detection algorithm, IFMBE Proc, 1727-1983.
[25] Fariha, M., Ikeura, R., Hayakawa, S., Tsutsumi, S. (2020). Analysis of Pan-Tompkins algorithm performance with noisy ECG signals, Journal of Physics: Conference Series, IOP Publishing, 012022.
[26] Bali, J., Nandi, A., Hiremath, P., Patil, P.G. (2018). Detection of sleep apnea in ECG signal using Pan-Tompkins algorithm and ANN classifiers, Compusoft, 7 2852-2861.
[27] Patel, A.M., Gakare, P.K., Cheeran, A. (2012). Real time ECG feature extraction and arrhythmia detection on a mobile platform, Int. J. Comput. Appl, 44 40-45.
[28] Gini, J.R., Ramachandran, K., Nair, R.H., Anand, P. (2016). Portable fetal ECG extractor from abdECG, 2016 International Conference on Communication and Signal Processing (ICCSP), IEEE, 0845-0848.