Foetal ECG recovery using dynamic neural networks

Artificial Intelligence in Medicine

Volumn 31, Issue 3, 2004, Pages 197-209

  Camps Valls, Gustavo ^a   Martínez Sober, Marcelino ^a   Soria Olivas, Emilio ^a   Magdalena Benedito, Rafael ^a   Calpe Maravilla, Javier ^a   Guerrero Martínez, Juan ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

Author keywords

Adaptive filtering; Cardiology; Dynamic neural network; Foetal ECG

Indexed keywords

ALGORITHMS; AUTOMATION; ELECTROCARDIOGRAPHY; FETAL MONITORING; LEAST SQUARES APPROXIMATIONS; SIGNAL TO NOISE RATIO;

DYNAMIC NEURAL NETWORKS; SYNTHETIC REGISTERS;

NEURAL NETWORKS;

ALGORITHM; ANALYSIS OF VARIANCE; ARTICLE; ARTIFICIAL NEURAL NETWORK; CORRELATION COEFFICIENT; FETUS ELECTROCARDIOGRAPHY; MATHEMATICAL MODEL; PRIORITY JOURNAL; REGISTER; SIGNAL NOISE RATIO; SIMULATION;

ELECTROCARDIOGRAPHY; FEMALE; FETAL HEART; HUMANS; MODELS, CARDIOVASCULAR; NEURAL NETWORKS (COMPUTER); PREDICTIVE VALUE OF TESTS; PREGNANCY; SENSITIVITY AND SPECIFICITY;

EID: 4043065618     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2004.03.005     Document Type: Article

References (46)

1. Deam A. The use of foetal electrocardiogram. Am. J. Obstet. Gynecol. 101:1994;9-17
2. Drose JA. Fetal echocardiography. Philadelphia, USA: W.B. Saunders Co.; 1998.
3. Jenkins H.M.L., Symonds E.M., Kirk D.L., Smith P.R. Can fetal electrocardiography improve the prediction of intrapartum fetal acidosis? Br. J. Obstet. Gynaecol. 93:1986;6-12
4. Karin J, Hirsch M, Sagiv C, Akselrod S. Fetal ANS activity by spectral analysis of HR variations. In: Murray A, editor. Proceedings of the Computers in Cardiology, Durham, North Carolina, USA. Piscataway, NJ, USA: IEEE Computer Society Press; 1992.
5. Horner SL. An effective algorithm for enhancing a non-invasive fetal electrocardiogram. Ph.D. Thesis. The University of Tennessee, Knoxville. USA; 1996.
6. Pardi G., Ferrazzi E., Cetin I., Rampello S., Baselli G., Cerutti S., et al. The clinical relevance of the abdominal fetal electrocardiogram during the antepartum period. Am. J. Obstet. Gynecol. 145:1983;566-569
7. Frank T.H., Blaumanis O.R., Chen S.H., Petrie R.H., Gibbs R.K., Wells R.L., et al. Noninvasive fetal ECG mode fetal heart rate monitoring by adaptive digital filtering. J. Perinat. Med. 20:1992;93-100
8. Frank T.H., Blaumanis O.R., Gibbs R.K., Wells R.K. Adaptive filtering in ECG monitoring of the fetal heart rate. J. Electrocardiol. 20(Suppl):1987;108- 113
9. Horner S.L., Holls W.M., Crilly P.B. A robust real-time algorithm for enhancing non-invasive foetal electrocardiogram. Digital Signal Process. 5:1995;184-194
10. Adam D., Shavit D. Complete foetal ECG morphology recording by synchronised adaptive filtration. Med. Biol. Eng. Comput. 1:1990;287-292
11. Bergvelt P., Meijer W.J.H. A new technique for the suppression of the MECG. IEEE Trans. Biomed. Eng. 28:1981;348-354
12. van Oosterom A. Spatial filtering of the fetal electrocardiogram. J. Perinat. Med. 14:1986;411-419
13. Longini R., Reichert T., Cho J., Crowley J. Near orthogonal basis functions: a real-time foetal ECG technique. IEEE Trans. Biomed. Eng. 24:1977;29-43
14. Callaerts D. Signal separation methods based on singular value decomposition and their application to the real-time extraction of the foetal electrocardigram from cutaneous recordings. Ph.D. Thesis. Katolieke Universiteit of Leuven. The Netherlands; 1989.
15. Vanderschoot J., Callaerts D., Sansen W., Vandewalle J., Vantrappen G., Janssens J. Two methods for optimal MECG elimination and FECG detection from skin electrode signals. IEEE Trans. Biomed. Eng. 34:1987;233-243
16. Kanjilal P.P., Palit S., Saha G. Fetal ECG extraction from single-channel maternal ECG using singular value decomposition. IEEE Trans. Biomed. Eng. 44:1997;51-59
17. Zarzoso V., Nandi A.K. Noninvasive fetal electrocardiogram extraction: blind separation versus adaptive noise cancellation. IEEE Trans. Biomed. Eng. 48:2001;12-18
18. Khamene A., Negahdaripour S. A new method for the extraction of fetal ECG from the composite abdominal signal. IEEE Trans. Biomed. Eng. 47:2000;507-516
19. Richter M., Schreiber T., Kaplan D.T. Fetal ECG extraction with nonlinear state-space projections. IEEE Trans. Biomed. Eng. 45:1998;133-137
20. Gibson N.M., Woolfson M.S., Crowe J.A. Detection of fetal electrocardiogram signals using matched filters with adaptive normalisation. Med. Biol. Eng. Comput. 35:1997;216-222
21. Budin N., Abbound S. Real-time multichannel abdominal foetal ECG monitor using a digital signal coprocessor. Comput. Biol. Med. 24:1994;451-462
22. Widrow B., Glover J., MacCool J., Kaunitz J., Williams C., Hearn R., et al. Adaptive noise cancelling: principles and applications. Proc. IEEE. 63:1975;1692-1716
23. Abboud M.F., Linkens D.A., Mahfouf M., Dounias G. Survey on the use of smart and adaptive engineering systems in medicine. Artif. Intell. Med. 26:2002;179-209
24. Haykin S. Adaptive filter theory. Englewood Cliffs, New Jersey, USA: Prentice-Hall; 1991.
25. Soria E., Martínez M., Calpe J., Guerrero J., Chorro J., Espí J. Algorithm for improving the performance of adaptive systems based on the LMS. J. Electr. Eng. 49:1998;270-272
26. Martínez M, Soria E, Calpe J, Guerrero J, Magdalena JR. Application of the adaptive impulse correlated filter for recovering fetal electrocardiogram. In: Murray A, Swiryn S, editors. Proceedings of the Computers in Cardiology 1997, Lund, Sweden. Piscataway, NJ, USA: IEEE Computer Society Press; 1997.
27. Haykin S. Neural networks: a comprehensive foundation. Englewood Cliffs, New Jersey, USA: Prentice-Hall; 1999.
28. Lin J.-H., Chang J.-S., Chiueh T.-D. Heterogeneous recurrent neural networks. IEICE Trans. Fundam. E81-A:1998;489-499
29. Camps G, Martínez M, Soria E. Fetal ECG extraction using an FIR neural network. In: Murray A, editor. Proceedings of the Computers in Cardiology 2001, Rotterdam, The Netherlands. Piscataway, NJ, USA: IEEE Computer Society Press; 2001.
30. Rumelhart DE, McClelland JL, PDP Research Group. Parallel distributed processing: explorations in the microstructure of cognition. Cambridge, MA: MIT Press; 1986.
31. Wan EA. Finite impulse response neural networks with applications in time series prediction. Ph.D. Thesis. Department of Electrical Engineering. Stanford University, USA; 1993.
32. Weigend AS, Gershenfeld NA. Time series prediction. Forecasting the future and understanding the past. Proceedings Volume XV, New Mexico; 1994. Proceedings of the NATO Advanced Research Workshop on Comparative Time Series Analysis held in Santa Fe, Addison-Wesley, New Mexico; 1992.
33. Weigend AS, Gershenfeld NA. Time series prediction. Forecasting the future and understanding the past. Proceedings Volume XV, New Mexico; 1994. Proceedings of the NATO Advanced Research Workshop on Comparative Time Series Analysis held in Santa Fe, Addison-Wesley, New Mexico; 1992.
34. de Vries B., Principe J.C. The Gamma model - a new neural model for temporal processing. Neural Netw. 5:1992;565-576
35. Cholewo TJ. Neural networks with dynamic synapses for time series prediction. Ph.D. Thesis. University of Louisville, Kentucky, USA; 1998.
36. Kam A, Cohen A. Separation of twins fetal ECG by means of blind source separation (BSS). In: IEEE Israel Section, editors. Proceedings of the 21st IEEE Convention of the Electrical and Electronic Engineers in Israel, Tel-Aviv. Piscataway, NJ, USA: IEEE Computer Society Press; 2000.
37. Martínez M, Martín J, Calpe J, Guerrero J, Soria E, Munoz J. Removing baseline wander and power line interference in non-invasive FECG registers. In: Orton CG, editor. Proceedings of the World Congress on Medical Physics and Biomedical Engineering, Chicago, Illinois, USA. Piscataway, NJ, USA: IEEE Computer Society Press; 2000.
38. Martínez M, Calpe J, Soria E, Guerrero J, Gómez L, Camps G. Methods to evaluate the performance of fetal electrocardiogram extraction algorithms. In: Murray A, editor. Proceedings of the Computers in Cardiology 2001, Rotterdam, The Netherlands. Piscataway, NJ, USA: IEEE Computer Society Press; 2001.
39. Principe J.C., deVries B., Oliveira P.G. The gamma filter - a new class of adaptive IIR filters with restricted feedback. IEEE Trans. Signal Process. 41:1993;649-656
40. Oliveira T. Generalized feed-forward filters: some theoretical results. In: IEEE Society, editors. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP93, vol. 3, Minneapolis, Minnesota, USA. Piscataway, NJ, USA: IEEE Computer Society Press; 1993. p. 109-12.
41. Kuo JM, Celebi S, Principe J. Adaptation of memory depth in the gamma filter. In: Morton, et al., editors. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP94. Piscataway, NJ, USA: IEEE Computer Society Press; 1994. p. 373-6.
42. Lim S-J, Harris JG. Delay lines using self-adapting time constants. In: IEEE Society, editors. Proceedings of the IEEE international conference on computational cybernetics and simulation, vol. 3, Orlando, FL, USA. Piscataway, NJ, USA: IEEE Computer Society Press; 1997. p. 2853-8.
43. Chiang TW. The analytical solution of gamma filter model. In: Gerstner W, Germond A, Nicoud MH, J-D, editors. Proceedings of the International Conference on Neural Networks, ICANN97, Texas, USA. Piscataway, NJ, USA: IEEE Computer Society Press; 1997.
44. Zarzoso V, Nandi AK. Comparison between blind separation and adaptive noise cancellation techniques for fetal electrocardiogram extraction. IEE colloquium on medical applications of signal processing, London. Institution of Electrical Engineers, IEE Savoy Place, London, UK; 1999.
45. Cardoso J.F., Laheld B.H. Equivariant adaptive source separation. IEEE Trans. Signal Process. 44:1996;3017-3030
46. Principe JC, Kuo J-M. Noise Reduction in state space using the focused gamma neural network. In: Morton, et al., editors. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, ICASSP'94, Adelaide, Australia. Piscataway, NJ, USA: IEEE Computer Society Press; 1994.