Abstract
A Fetal Electrocardiogram (FECG) signal will contain some potential information which is precise for assisting the clinicians to make appropriate decisions that are timely at the time of pregnancy and labor. Extraction and detection of such FECG signals from their composite maternal signals of the abdomen using very powerful and advanced methodologies is a very critical need in case of fetal monitoring. The mining of pure FECG signals from the abdominal of the prenatal woman is focused in this paper. This FECG signal is very vulnerable to noise and is very difficult to process it accurately with no significant distortion that can imped its use. So, in order to be able to get some proper information on the status of the fetus or its condition it is important to be able to improve the abdominal signal and its SNR. Since the wavelet transform is well in providing information in time and frequency, the combination of Stationary Wavelet Transform (SWT), Weighted Least Square Regression (WLSR) and parallel Extended Kalman Filter (Par-EKF) are used for FECG extraction. The analysis for different values of Signal to Noise Ratio (SNR) between fetal and maternal ECG’s is implemented using MATLAB. As a Result, the highest SNR obtained is 33.5 which showed that the proposed method is efficient in extracting more information about FECG from Maternal ECG (MECG).
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Yazbeck, A.S.: Challenges in measuring maternal mortality. Lancet. 370(9595), 1291–1292 (2007)
Sameni, R., Clifford, G.D.: A review of fetal ECG signal processing; issues and promising directions. Open Pacing Electrophysiol Ther. J. 3, 4 (2010)
Assaleh, K.: Extraction of fetal electrocardiogram using adaptive neuro-fuzzy inference systems. IEEE Trans. Biomed. Eng. 54(1), 59–68 (2007)
Sato, M., Kimura, Y., Chida, S., et al.: A novel extraction method of fetal electrocardiogram from the composite abdominal signal. IEEE Trans. Biomed. Eng. 54(1), 49–58 (2007)
De Lathauwer, L., De Moor, B., Vandewalle, J.: Fetal electrocardiogram extraction by blind source subspace separation. IEEE Trans. Biomed. Eng. 47(5), 567–572 (2000)
Khamene, A., Negahdaripour, S.: A New Method for Extraction of Fetal ECG from the Composite Abdominal Signal. IEEE Trans. Biomed. Eng. 47(4), 507–516 (2000)
Ibrahimy, M.I., Ahmed, F., Ali, M.M., Zahedi, E.: Real-time signal processing for fetal heart rate monitoring. IEEE Trans. Biomed. Eng. 50(2), 258–261 (2003)
Karvounis, E., Papaloukas, C., Fotiadis, D.I., Michalis, L.K.: Fetal heart rate extraction from composite maternal ECG using complex continuous wavelet transform. Comput. Cardiol. 31, 737–740 (2004)
Vigneron, V., Paraschiv-Ionescu, A., Azancot. A., Siboney, O., Jutten, C.: Fetal electrocardiogram extraction based on non-stationary ICA and wavelet denoising. In: Signal Proceedings and its Applications, Seventh International Symposium, vol. 2. IEEE (2003)
Assaleh, K.T., Al-Nashash H.A.: A novel technique for the extraction of fetal ECG using polynomial networks. IEEE Trans. Biomed. Eng. 52(6), 1148–1152 (2005), ISSN: 0018-9294.
Azzerboni, B., La Foresta, F., Mammone, N., Morabito, F.C.: A new approach based on Wavelet-ICA algorithm for fetal electrocardiogram extraction. In: EASNN, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 193–198 (2005)
Mahmoodabadi, S.Z., Ahmadian, A., Abolhasani, M.D.: ECG extraction using Daubechies wavelets. In: Proceedings of the Fifth IASTED International Conference on Visualization, Imaging and Image Processing, pp. 343–348 (2005)
Vullings, R., Peters, C., Mischi, M., Oei, G., Bergmans, J.: Maternal ECG removal from non-invasive fetal ECG recordings (2006)
Assaleh, K.: Extraction of fetal electrocardiogram using adaptive neuro-fuzzy inference system. IEEE Trans. Biomed. Eng. 54(1), 59–68 (2007), ISSN: 0018-9294.
Mortazavi, S.H., Shahrtash, S.M.: Comparing denoising performance of DWT, WPT, SWT and DT-CWT for partial discharge signals. In: Proceedings of the 43rd International Universities Power Engineering Conference, pp. 1–6 (2008).
Sifuzzaman, M., Islam, M.R., Ali, M.Z.: Application of wavelet transform and its advantages compared to fourier transform. J. Phys. Sci. 13, 121–134 (2009), ISSN: 0972-8791.
Horng, S.C.: Compensating modeling overlay errors using the weighted least-squares estimation. IEEE Trans. Semicond. Manuf. 27(1), 60–70 (2014)
Sameni, R.: Extraction of fetal cardiac signals from an array of maternal abdominal recordings, Ph.D. thesis, Sharif University of Technology—Institut National Polytechnique de Grenoble (2008)
Sameni, R., Shamsollahi, M.B., Jutten, C., Clifford, G.D.: A nonlinear Bayesian filtering framework for ECG denoising. IEEE Trans. Biomed. Eng. 54(12), 2172–2185 (2007)
Niknazar, M., Rivet, B., Jutten, C.: Fetal ECG extraction by extended state Kalman Filtering based on single-channel recordings. IEEE Trans. Biomed. Eng. 60(5), 1345–1352 (2013)
De Moor, B., De Gersem, P., De Schutter, B., Favoreel, W.: DAISY: a database for identification of systems. J. A. 38(3), 4–5 (1997)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Suganthy, M., Manjula, S. Enhancement of SNR in fetal ECG signal extraction using combined SWT and WLSR in parallel EKF. Cluster Comput 22 (Suppl 2), 3875–3881 (2019). https://doi.org/10.1007/s10586-018-2477-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-018-2477-4