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Systematic analysis of single- and multi-reference adaptive filters for non-invasive fetal electrocardiography

1 Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari 09123, Italy
2 Division of Pediatric Cardiology, S. Michele Hospital, Cagliari 09123, Italy

Special Issues: Computer Methods and Programs in Prenatal Medicine

Non-invasive fetal electrocardiography (ECG) has been a research challenge for the past few decades. Due to instrumental noise and the spectral overlap of the maternal ECG signal, the signal-to-noise ratio for fetal ECG is very low. Various techniques have been proposed for cancelling the maternal ECG signal and extracting the fetal QRS complex from non-invasive abdominal recordings. Of these, adaptive filters enable satisfactory extraction when there is only a limited number of signal channels available, but the extraction quality is strongly dependent on the electrode placement. In this work, we systematically analyze this issue by comparing single- and multi-reference implementations of QRD-recursive least square (RLS) adaptive filters and evaluating their performances on real and simulated data in terms of the signal-to-interference ratio (SIR), maternal ECG attenuation, and fetal-QRS-complex detection accuracy. Beyond demonstrating the expected superior performance of the multi-reference version (p < 0.05) with respect to all metrics, except the QRS detection accuracy on synthetic data, we also analyze in detail the effectiveness of this technique with different lead orientations with respect to the correct interpretation of the adopted quality indexes. The results reveal that the single-reference approach, which is preferred when only the fetal heart rate is of interest, cannot produce a signal that has acceptable fetal QRS detection accuracy, regardless of the reference lead selection.
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References

1. M. G. Signorini, A. Fanelli and G. Magenes, Monitoring fetal heart rate during pregnancy: Contributions from advanced signal processing and wearable technology, Comput. Math. Methods Med., 2014 (2014).

2. American College of Obstetricians and Gynecologists, American college of obstetricians and gynecologists intrapartum fetal heart rate monitoring: Nomenclature, interpretation, and general management principles; ACOG practice bulletin no. 106, Obstet. Gynecol., 114 (2009), 192-202.

3. M. Ferrario, M. G. Signorini and G. Magenes, Complexity analysis of the fetal heart rate variability: early identification of severe intrauterine growth-restricted fetuses, Med. Biol. Eng. Comput., 47 (2009), 911-919.

4. D. Hoyer, J. Żebrowski, D. Cysarz, et al., Monitoring fetal maturation-objectives, techniques and indices of autonomic function, Physiol. Meas., 38 (2017), R61-R88.

5. U. Schneider, F. Bode, A. Schmidt, et al., Developmental milestones of the autonomic nervous system revealed via longitudinal monitoring of fetal heart rate variability, PLoS One, 13 (2018), 1-13.

6. R. Weber, D. Stambach and E. Jaeggi, Diagnosis and management of common fetal arrhythmias, J Saudi Hear. Assoc., 23 (2011), 61-66.

7. M. T. Donofrio, A. J. Moon-Grady, L. K. Hornberger, et al., Diagnosis and treatment of fetal cardiac disease: A scientific statement from the american heart association, Circulation, 21 (2014), 2183-2242.

8. A. Sacco, J. Muglu, R. Navaratnarajah, et al., ST analysis for intrapartum fetal monitoring, Obstet. Gynaecol., 17 (2015), 5-12.

9. A. Dessì, D. Pani and L. Raffo, An advanced algorithm for fetal heart rate estimation from non-invasive low electrode density recordings, Physiol. Meas., 35 (2014), 1621.

10. T. F. Oostendorp, A. van Oosterom and H. W. Jongsma, Electrical properties of tissues involved in the conduction of foetal ECG, Med. Biol. Eng. Comput., 27 (1989), 322-324.

11. R. Sameni and G. D. Clifford, A review of fetal ECG signal processing; issues and promising directions, Open Pacing. Electrophysiol. Ther. J, 3 (2010), 4-20.

12. V. Zarzoso and A. K. Nandi, Noninvasive fetal electrocardiogram extraction: Blind separation versus adaptive noise cancellation, IEEE Trans. Biomed. Eng., 48 (2001), 12-18.

13. S. Muceli, D. Pani and L. Raffo, Non-invasive real-time fetal ECG extraction - A block-on-line DSP implementation based on the JADE algorithm, Proceedings of the 1st International Conference on Bio-inspired Systems and Signal Processing, 2 (2008), 458-463. Available from: https://iris.unica.it/handle/11584/107493.

14. D. Pani, S. Argiolas and L. Raffo, A DSP algorithm and system for real-time fetal ECG extraction, 2008 Computers in Cardiology, 35 (2008), 1065-1068. Available from: https://ieeexplore_ieee.gg363.site/abstract/document/4749229.

15. D. Pani, G. Barabino and L. Raffo, NInFEA: An embedded framework for the real-time evaluation of fetal ECG extraction algorithms, Biomed. Tech., 58 (2013), 13-26.

16. B. Widrow and S. D. Stearns, Adaptive Signal Processing, Englewood Cliffs, NJ: Prentice-hall, 1985.

17. M. Shadaydeh, Y. Xiao and R. Ward, Extraction of fetal ECG using adaptive volterra filters, 2008 16th European Signal Processing Conference, (2008), 1-5. Available from: https://ieeexplore_ieee.gg363.site/abstract/document/7080365.

18. R. Martinek, R. Kahankova, H. Skutova, et al., Adaptive signal processing techniques for extracting abdominal fetal electrocardiogram, 2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), (2016), 1-6. Available from: https://ieeexplore_ieee.gg363.site/abstract/document/7573974.

19. R. Kahankova, R. Martinek and P. Bilik, Fetal ECG extraction from abdominal ECG using RLS based adaptive algorithms, 2017. 18th International Carpathian Control Conference, (2017), 337-342. Available from: https://ieeexplore_ieee.gg363.site/abstract/document/7970422.

20. J. Behar, A. Johnson, G. D. Clifford, et al., A comparison of single channel fetal ECG extraction methods, Ann. Biomed. Eng., 42 (2014), 1340-1353.

21. S. Rajaguru and D. V. Prasad, A novel technique for extraction of FECG using multi stage adaptive filtering, J. Appl. Sci., 10 (2010), 319-324.

22. S. Ravindrakumar and K. Bommannaraja, Certain investigation on De-noising the multichannel abdominal ECG signal using various adaptive noise suppression techniques, Aust. J. Basic Appl. Sci., (2015), 372-380.

23. I. Silva, J. Behar, R. Sameni, et al., Noninvasive cetal ECG: The physionet/computing in cardiology challenge 2013, Computing in Cardiology 2013, 40 (2013), 149-152. Available from: https://ieeexplore_ieee.gg363.site/abstract/document/6712433.

24. R. Rodrigues, Fetal beat detection in abdominal ECG recordings: global and time adaptive approaches, Physiol. Meas., 35 (2014), 1699-1711.

25. S. L. Lima-Herrera, C. Alvarado-Serrano and P. R. Hernández-Rodríguez, Fetal ECG extraction based on adaptive filters and Wavelet Transform: Validation and application in fetal heart rate variability analysis, 2016 13th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), (2016), 1-6. Available from: https://ieeexplore_ieee.gg363.site/abstract/document/7751243.

26. J. Behar, F. Andreotti, S. Zaunseder, et al., An ECG simulator for generating maternal-foetal activity mixtures on abdominal ECG recordings, Physiol. Meas., 35 (2014), 1537.

27. E. Sulas, E. Ortu, L. Raffo, et al., Automatic recognition of complete atrioventricular activity in fetal pulsed-wave doppler signals, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 18 (2018), 917-920. Available from: https://ieeexplore_ieee.gg363.site/abstract/document/8512329.

28. F. Andreotti, J. Behar, S. Zaunseder, et al., An open-source framework for stress-testing non-invasive foetal ECG extraction algorithms, Physiol. Meas., 37 (2016), 627-648.

29. A. H. Sayed, Adaptive Filters, John Wiley & Sons, 2011.

30. N. Chaitra, G. PraveenKumarY and M. Z. Kurian, Design and implementation of high performance adaptive FIR filter systems using QRD-RLS method,Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 3 (2014), 2320-3765.

31. J. A. Apolinário and R. Rautmann, QRD-RLS Adaptive Filtering, Ed. José Antonio Apolinário, New York: Springer, 2009.

32. P. Kligfield, L. S. Gettes, J. J. Bailey, et al., Recommendations for the standardization and interpretation of the electrocardiogram: part I: The electrocardiogram and its technology a scientific statement from the american heart association electrocardiography and arrhythmias committee, council on clinical cardiology; the american college of cardiology foundation; and the heart rhythm society endorsed by the international society for computerized electrocardiology, J. Am. Coll. Cardiol., 49 (2007), 1109-1127.

33. J. Pan and W. J. Tompkins, A real-time QRS detection algorithm,IEEE Trans. Biomed. Eng., 3 (1985), 230-236.

34. M. J. O. Taylor, M. J. Smith, M. Thomas, et al., Non-invasive fetal electrocardiography in singleton and multiple pregnancies, BJOG, 110 (2003), 668-678.

35. B. Surawicz, R. Childers, B. J. Deal, et al., AHA/ACCF/HRS Recommendations for the standardization and interpretation of the electrocardiogram, J. Am. Coll. Cardiol., 119 (2009), e235-e240.

36. J. Behar, J. Oster and G. D. Clifford, Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data, Physiol. Meas., 35 (2014), 1569-1589.

37. S. M. M. Martens, C. Rabotti, M. Mischi, et al., A robust fetal ECG detection method for abdominal recordings, Physiol. Meas., 28 (2007), 373-388.

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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