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A Review of Techniques for Foetal Electrocardiogram Extraction

by Nishant Aggarwal, Butta Singh
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 4 - Number 9
Year of Publication: 2016
Authors: Nishant Aggarwal, Butta Singh
10.5120/cae2016652175

Nishant Aggarwal, Butta Singh . A Review of Techniques for Foetal Electrocardiogram Extraction. Communications on Applied Electronics. 4, 9 ( April 2016), 41-47. DOI=10.5120/cae2016652175

@article{ 10.5120/cae2016652175,
author = { Nishant Aggarwal, Butta Singh },
title = { A Review of Techniques for Foetal Electrocardiogram Extraction },
journal = { Communications on Applied Electronics },
issue_date = { April 2016 },
volume = { 4 },
number = { 9 },
month = { April },
year = { 2016 },
issn = { 2394-4714 },
pages = { 41-47 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume4/number9/582-2016652175/ },
doi = { 10.5120/cae2016652175 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:53:54.864865+05:30
%A Nishant Aggarwal
%A Butta Singh
%T A Review of Techniques for Foetal Electrocardiogram Extraction
%J Communications on Applied Electronics
%@ 2394-4714
%V 4
%N 9
%P 41-47
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Electrocardiogram (ECG) holds high significance in medical diagnostics. Cardiologists consider it as an enduring tool and thus the improvement of the diagnostic quality the signal for various recognitions in different environments become a challenge. The signal acquisition is susceptible to the interference from physiological as well as environmental sources. Foetal ECG provides vital information to the physicist to assist in taking critical decisions especially during labor time. Since the direct contact over foetus is perilous to its health, foetal ECG acquisition becomes a challenging task. There is a time as well as frequency overlap of the stronger maternal ECG over the weak foetal ECG. Thus windowing and simple filtering does not extract these signals. This has encouraged various researchers to dwell deep into innovating such filtering techniques to make the acquired signal qualify for discrete diagnostics. This work focuses on the various algorithms proposed for the foetal extraction in terms of their capabilities and performances.

References
  1. Jagannath, D. J., and Selvakumar, A. I. 2014. Issues and research on foetal electrocardiogram signal elicitation, Biomedical Signal Processing and Control. 10, 224–244.
  2. Gupta, P., Sharma,K.K., and Joshi, S.D. 2016. Fetal heart rate extraction from abdominal electrocardiograms through multivariate empirical mode decomposition. Computers in Biology and Medicine. 68,121–136.
  3. Karvounis, E.C., Papaloukas, C., Fotiadis, D.I. and Michalis, L.K. 2004. Fetal heart rate extraction from composite maternal ECG using complex continuous wavelet transform, Computer Cardio 1,737–740.
  4. Song, Y., Xie, W., and Chen, J.F. 2006. Passive acoustic maternal abdominal fetal heart rate monitoring using wavelet transform. Computer Cardio,581–584.
  5. Sargolzaei, S., Faez, K., and Sargolzaei,A. 2008. Signal processing based for fetal electrocardiogram elicitation. In Proceedings of the International Conference on Biomedical Engineering and Informatics.
  6. Zibulevsky, M., and Zeevi Y.Y. 2002. Extraction of a source from multichannel data using sparse decomposition. Neurocomputing 49 (1–4),163–173.
  7. Liu, W., and Mandic, D.P. 2006. A normalized kurtosis based blind source extraction from noisy mixture. Signal Process. 86(7),1580–1585.
  8. Zhang, Z.L., and Yi, Z. 2006. Extraction of a source signal whose kurtosis value lies in a specific range, Neurocomputing 69 (7–9),900–904.
  9. Lathauwer, L., Moor, B., and Vandewalle, J. 2000. Fetal electrocardiogram extraction by blind source subspace separation. IEEE Trans. Biomed. Eng. 47(5),567–572.
  10. Cichocki, A., and Thawonwas, R. 2000. On-line algorithm for blind signal extraction of arbitrarily distributed, but temporally correlated sources using second order statistics. Neural Process. Lett. 12 (1),91–98.
  11. Zhang, Z.L., and Yi, Z. 2006. Robust extraction of specific signals with temporal structure. Neurocomputing 69 (7–9), 888–893.
  12. Barros, A.K., and Cichocki, A. 2001. Extraction of specific signals with temporal structure. Neural Comput. 13 (9), 1995–2003.
  13. Yunxia, L., and Zhang, Y. 2008.An algorithm for extracting fetal electrocardiogram. Neurocomputing 71, 1538–1542.
  14. Hasan, M.A. and Reaz,I. 2009. Detection and Processing Techniques of FECG Signal for Fetal Monitoring. Biological Procedures Online. vol.11(1),263-295.
  15. Raj, J., Prabhu, V., Christophera, J., Sugumar, D., and Vanathi, P.T. 2012. Separation Of Maternal And Fetal ECG Signals From The Mixed Source Signal Using FASTICA, International Conference on Communication Technology and System Design 2011 Procedia Engineering 30 , 356 – 363.
  16. Clemente, R.M., Olivares, J.L., Mellado, S.H., and Roman, M.I. 2011.Fast Technique for Noninvasive Fetal ECG Extraction, IEEE transactions on biomedical engineering vol. 58. no. 2.
  17. Mihaela, G., Johannes, U., Bergmansb, W.M., Guid, O.S., Ungureanua, A., and Wolfd, W. 2009. The event synchronous canceller algorithm removes maternal ECG from abdominal signals without affecting the fetal ECG,Computers in Biology and Medicine 39, 562 – 567.
  18. Widrow, B., Glover, J.R., McCool, J.M., Kaunitz, J., Williams, C.S., Hearn, R.H., Zeidler, J.R., Dong, E. and Goodlin, R.C. 1975. Adaptive noise cancelling: principles and applications, Proc. IEEE 63 (12),1692–1716.
  19. Bergveld,P., and Meijer, W.H.J. 1981.A new Technique for the suppression of the MECG,IEEE Trans. Biomed. Eng. 28 (4),348–354.
  20. Levkov,C., Mihov, G., Ivanov, R., Daskalov, I., Christov, I., and Dotsinsky I. 2005.Removal of power-line interference from the ECG: a review of the subtraction procedure. BioMed. Eng. Online 4 (4),50.
  21. Van,J.H., and Bemmel. 1968. Detection of weak foetal electro-cardiograms by autocorrelation and cross correlation of envelopes. IEEE Trans. Biomed. Eng. BME-15 (1),17–23.
  22. Shi, Z., and Zhang, C. 2007. Semi-blind source extraction for fetal electrocardiogram extraction by combining non-Gaussianity and time-correlation. Neurocomputing 70 (7-9), 1574–1581.
  23. Wu,S., Shen,Y., Zhou,Z., Lin,L., and Zeng,Y. Gao,X. 2013. Research of fetal ECG extraction using wavelet analysis and adaptive filtering. Computers in Biology and Medicine 43, 1622–1627.
  24. Rahman, M.Z.U., Shaik, R.A., and Reddy, R K. 2011. Efficient sign based normalized adaptive filtering techniques for cancelation of artifacts in ECG signals: application to wireless biotelemetry. Signal Process. 91 (2),225–239.
  25. Valls, G.C., Sober, M.M., Rafael, E.S., Benedito, M., Maravilla, J.C., and Martinez, J.G. 2004. Foetal ECG recovery using dynamic neural networks. Artificial Intelligence in Medicine 31, 197—209.
  26. Hasan,M., and Reaz,M. 2012. Hardware prototyping of neural network based fetal electrocardiogram extraction. Meas. Sci. Rev. 12,52–55.
  27. Pu, X.J., Zeng, X.P., Chen, Y.J., Yu, W., Han, L., and Cheng, J. 2009. Fetal electrocardiogram extraction based on radial basis function neural networks, J. Chongqing Univ.32,111–115.
  28. Camps, G., Martínez, M., and Soria,E. 2001.Fetal ECG Extraction using an FIR Neural Network. Computers in Cardiology,249–252.
  29. Assaleh, K. 2007.Extraction of Fetal Electrocardiogram Using Adaptive Neuro-Fuzzy Inference Systems. IEEE transactions on biomedical engineering3.vol. 54. no. 1.
  30. Vijila, C. K.S., Kanagasabapathy, P. and Johnson, S. 2005. Adaptive neuro fuzzy inference system for elicitation of fECG. Annual IEEE India Conference-indicon, 224–227.
  31. Azad, K.A.K. 2000. Fetal QRS complex detection from abdominal ECG: a fuzzy approach. In the proceedings of IEEE Nordic Signal Processing Symposium, Kolmarden. Sweden, 275–278.
  32. Neves, Fernandes, J.A.C. and Restivo, F. 2006. Phase space signal filtering. Proc. IEEE ICIT, 1805–1809.
  33. Wu, Ji, Jiang, Q.H., and Tang, L.2011. Disturbance detection location and classification in phase space. IET Gener. Trans. Distrib. 5(2), 257–265.
  34. Wei, Z., Hongxing,L., and Jianchun,C. 2012. Adaptive filtering in phase space for foetal electrocardiogram estimation from an abdominal electrocardiogram signal and a thoracic electrocardiogram signal. IET Signal Processing. Vol. 6, Issue. 3, 171–177.
  35. Wei, Z., Xueyun, W., Jian, Z., and Hongxing, L. 2013. Noninvasive fetal ECG estimation using adaptive comb filter, computer methods and programs in biomedicine 112,125–134.
  36. Wei, Z., Hongxing, L., Aijun, H., Xinbao, N., and Jianchun,C. 2010. Single-lead fetal electrocardiogram estimation by means of combining R-peak detection, resampling and comb filter.Medical Engineering & Physics 32 (7),708–719.
  37. Frazier, R.H., Samsam, S., Braida, L.D., and Oppenheim, A.V. 1976.Enhancement of speech by adaptive filtering. In the Proceedings of IEEE Int. Conf. on Acoust. Speech, and Signal Processing,Philadelphia.PA API(12–14),251–253.
  38. Lim, J.S., Oppenheim, A.V. and Braida, L.D. 1978. Evaluation of an adaptive comb filtering method for enhancing speech degraded by white noise addition. IEEE Transactions on Acoustics Speech and Signal Processing.ASSP-26 (August,1978), 354–358.
  39. Oikonomou, V.P. and Fotiadis, D.I. 2005. A Bayesian PCA approach for fetal ecg extraction.In the proceedings of the 3rd European Medical and Biological Engineering Conference EMBEC’05. November 20-25. Prague. Czech Republic. vol. 11. no. 1, 1–5.
  40. Sameni, R., Shamsollahi, M.B., Jutten,C. and Clifford,G.D. 2007. A nonlinear bayesian filtering framework for ECG denoising. In the proceedings of IEEE Trans. Biomed. Eng.
  41. Yin, Y., Ye, M., Ren, D., Zhu, Y., and Yang, C. 2010.FECG extraction using bayesian inference and neural networks approximation. Computat. Inform. Syst. 6, 1769–1778.
  42. Vullings, R., Vries, B. and Bergmans, J.W.M. 2011. An adaptive kalman filter for ECG signal enhancement. IEEE Trans. Biomed. Eng. 58 (4), 1094–1103
  43. Karvounis, E. C., Tsipouras, M. G., Papaloukas, C., Tsalikakis, D. G. and Naka, K. K., Fotiadis,D.I. 2010.A non-invasive methodology for fetal monitoring during pregnancy, Methods Inform. Med. 49 (3), 238–253.
  44. Ungureanu, M., Bergmans, J.W.M., Oei,S.G., and Strungaru, R. 2007. Fetal ECG elicitation during labor using an adaptive maternal beat subtraction technique. Biomedizinische Technik Bio Med. Eng. 52 (1),56–60.
  45. Azad, K.A.K. 2000. Fetal QRS complex detection from abdominal ECG: a fuzzy approach. In the proceedings of IEEE Nord. Signal Process. Symp. Kolmarden .Sweden, 275–278.
  46. Piere, J.F, Crowe, J.A., Hayes-Gill, B.R., Spencer, C.J., Bhogal, K., and James, D.K. 2001. Compact long-term recorder for the trans- abdominal foetal and maternal electrocardiogram. Med. Biol. Eng. Comput. 39.118–125.
  47. Ibrahimy, M.I., Ahmed, F., Ali, M.A.M., and Zahedi, E. 2003. Real-time signal processing for fetal heart rate monitoring. IEEE Trans. Biomed. Eng. 50 ,258–262.
  48. Martens, S.M.M., Rabotti, C., Mischi, M., and Sluijter, R.J. 2007. A robust fetal ECG detection method for abdominal recordings. Physiol. Meas. 28, 373–388.
  49. Karvounis, E.C., Tsipouras, M.G., Fotiadis, D.I., and Naka, K.K. 2007. An automated methodology for fetal heart rate extraction from the abdominal electrocardiogram. IEEE Trans. Inf. Technol. Biomed. 11,628–638.
  50. Karvounis, E.C., Tsipouras, M.G., and Fotiadis, D.I. 2009. Detection of fetal heart rate through 3-D phase space analysis from multivariate abdominal recordings. IEEE Trans. Biomed. Eng. 56, 1394–1406.
Index Terms

Computer Science
Information Sciences

Keywords

Adaptive algorithm ECG foetal non-adaptive algorithm non-invasive

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