Abstract
Electrocardiogram (ECG) signals are one of the most important diagnostic tools for any doctor, especially a cardiologist. It is important that the fetus present inside the abdomen undergoes a fetal ECG recording to assess the health of the fetus. Complications like disturbance because of movement of abdominal muscles are usually present during the recording and leads to the wrong diagnosis of the fetus ECG. In this paper, the signal in dispute had been altered in the proposed method so as to eliminate the wandering of the baseline, respiration noise and also expel the noise from other sources. The acquired abdominal ECG signal in a noninvasive manner had been considered for extracting the fetal ECG after eliminating the noise. The windowed zero mean method is used where the first step is segmentation. In segmentation, the abdominal ECG signal is divided into set of samples based on window size. Zero mean is applied across each of the windowed abdominal ECG signals to address the issue of baseline wandering and respiration noise. This is followed by the application of a bandpass filter to cancel the high-frequency noise component. This process results in an ECG signal that almost has no complications as present before. The fetal ECG signal that is procured using such a method is now easier to diagnose as compared to the acquired signal which contains noise. Thus, for a fetus, this can help in proper diagnosis. It is further noted that this method is very reliant on using and is lucid. It can be used to augment and alter signals where such complications arise in the field of medicine and clinical diagnosis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mehta, R.S.: ECG and cardiac arrhythmias (2014). https://www.slideshare.net/rsmehta/ecg-arrhythmias
UN Inter-agency Group for Child Mortality estimation: Mortality rate, neonatal (per 1,000 livebirths) (2015). http://data.worldbank.org/indicator/SH.DYN.NMRT
Kurinczuk, J.J., et al.: The contribution of congenital anomalies to infant mortality. https://www.npeu.ox.ac.uk/downloads/files/infant-mortality/Infant-Mortality-Briefing-Paper-4.pdf
Eliasson, H., Sonesson, S.E., Sharland, G., et al.: For the Fetal Working Group of the European Association of Pediatric Cardiology: isolated atrioventricular block in the fetus: a retrospective, multinational, multicenter study of 175 patients, vol. 124, pp. 1919–1926, October 2011. doi:10.1161/CIRCULATIONAHA.111.041970
Bar-Cohen, Y., Loeb, G.E., Pruetz, J.D., Silka, M.J., Guerra, C., Vest, A.N., Zhou, L., Chmait, R.H.: Preclinical testing and optimization of a novel fetal micropacemaker. Heart Rhythm (2015). doi:10.1016/j.hrthm.2015.03.022
Gini, J.R., Ramachandran, K.I., Nair, R.H., Anand, P.: Portable fetal ECG extractor from abdECG. In: International Conference on Communication and Signal Processing, pp. 0845–0848, April 2016
Sarkar, S., Bhattacherjee, S., Pal, S.: Extraction of respiration signal from ECG for respiratory rate estimation. In: Michael Faraday IET International Summit, pp. 336–340, September 2015
Park, S.B., Noh, Y.S., Park, S.J., et al.: Med. Bio. Eng. Comput. 46, 147 (2008). doi:10.1007/s11517-007-0302-y
Yacoub, S., Raoof, K.: Noise removal from surface respiratory EMG signal. Int. J. Elect. Comp. Energ. Electron. Commun. Eng. 2(2), 266–273 (2008)
De Luca, C.J., Gilmore, L.D., Kuznetsov, M., Roy, S.H.: Filtering the surface EMG signal: movement artefact and baseline noise contamination. J. Biomech. 43(8), 1573–1579 (2010)
Golabbakhsh, M., Masoumzadeh, M., Sabahi, M.F.: ECG and power line noise removal from respiratory EMG signal using adaptive filters. Majlesi J. Elect. Eng. 5(4), 28–33 (2011)
Blanco-Velasco, M., Weng, B., Barner, K.E.: ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput. Biol. Med. 38, 1–13 (2008)
Huang, N.E., Shen, S.S.P.: Introduction to the Hilbert-Huang transform and its related mathematical problems. In: Hilbert-Huang Transform and Its Applications, 2nd edn., pp. 1–11. Abbrev. of Publisher, Singapore (2014). Chap. 1, Sect. 1.2
Van Alste, J.A., Schilder, T.S.: Removal of base-line wander and power-line interference from the ECG by an efficient FIR filter with a reduced number of taps. IEEE Trans. Biomed. Eng. 32(12), 1052–1060 (1985)
Pandey, V., Giri, V.K.: High frequency noise removal from ECG using moving average filters. In: International Conference on Emerging Trends in Electrical, Electronics and Sustainable Energy Systems, pp. 191–195, March 2016
Zhang, D.: Wavelet approach for ECG baseline wander correction and noise reduction. In: 27th Annual Conference of IEEE Engineering in Medicine and Biology, Shangai, pp. 1212–1215 (2005)
Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, PCh., 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), e215–e220 (2000). Circulation Electronic Pages, http://circ.ahajournals.org/content/101/23/e215.full
Kotas, M., Jezewski, J., Horoba, L., Matonia, A.: Application of spatio-temporal filtering to fetal electrocardiogram enhancement. Comput. Methods Progr. Biomed. 104(1), 1–9 (2011)
Rakhimov, A.: Normal respiratory frequency, volume, chart. http://www.normalbreathing.com/index-nb.php
Kathirvel, P., Sabarimalai Manikandan, M., Prasanna, S.R.M., Soman, K.P.: An efficient R-peak detection based on new nonlinear transformation and first-order gaussian differentiator. Cardiovasc. Eng. Technol. 2(4), 408–425 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Joseph, J., Gini, J.R., Ramachandran, K.I. (2018). Removal of BW and Respiration Noise in abdECG for fECG Extraction. In: Thampi, S., Krishnan, S., Corchado Rodriguez, J., Das, S., Wozniak, M., Al-Jumeily, D. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2017. Advances in Intelligent Systems and Computing, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-319-67934-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-67934-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67933-4
Online ISBN: 978-3-319-67934-1
eBook Packages: EngineeringEngineering (R0)