Open Access

Multichannel ECG and Noise Modeling: Application to Maternal and Fetal ECG Signals

  • Reza Sameni1, 2Email author,
  • Gari D Clifford3,
  • Christian Jutten2 and
  • Mohammad B Shamsollahi1
EURASIP Journal on Advances in Signal Processing20072007:043407

https://doi.org/10.1155/2007/43407

Received: 1 May 2006

Accepted: 2 November 2006

Published: 16 January 2007

Abstract

A three-dimensional dynamic model of the electrical activity of the heart is presented. The model is based on the single dipole model of the heart and is later related to the body surface potentials through a linear model which accounts for the temporal movements and rotations of the cardiac dipole, together with a realistic ECG noise model. The proposed model is also generalized to maternal and fetal ECG mixtures recorded from the abdomen of pregnant women in single and multiple pregnancies. The applicability of the model for the evaluation of signal processing algorithms is illustrated using independent component analysis. Considering the difficulties and limitations of recording long-term ECG data, especially from pregnant women, the model described in this paper may serve as an effective means of simulation and analysis of a wide range of ECGs, including adults and fetuses.

[12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849]

Authors’ Affiliations

(1)
Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology
(2)
Laboratoire des Images et des Signaux (LIS), CNRS - UMR 5083, INPG, UJF
(3)
Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences and Technology (HST), Massachusetts Institute of Technology

References

  1. Dössel O: Inverse problem of electro- and magnetocardiography: review and recent progress. International Journal of Bioelectromagnetism 2000.,2(2):Google Scholar
  2. van Oosterom A: Beyond the dipole; modeling the genesis of the electrocardiogram. In 100 Years Einthoven. The Einthoven Foundation, Leiden, The Netherlands; 2002:7-15.Google Scholar
  3. Malmivuo JA, Plonsey R (Eds): Bioelectromagnetism, Principles and Applications of Bioelectric and Biomagnetic Fields. Oxford University Press, New York, NY, USA; 1995.Google Scholar
  4. Oostendorp T: Modeling the fetal ECG, Ph.D. dissertation.Google Scholar
  5. Kanjilal PP, Palit S, Saha G: Fetal ECG extraction from single-channel maternal ECG using singular value decomposition. IEEE Transactions on Biomedical Engineering 1997,44(1):51-59. 10.1109/10.553712View ArticleGoogle Scholar
  6. Gao P, Chang E-C, Wyse L: Blind separation of fetal ECG from single mixture using SVD and ICA. Proceedings of the Joint Conference of the 4th International Conference on Information, Communications and Signal Processing, and the 4th Pacific Rim Conference on Multimedia (ICICS-PCM '03), December 2003, Singapore 3: 1418-1422.Google Scholar
  7. Callaerts D, Sansen W, Vandewalle J, Vantrappen G, Janssen J: Description of a real-time system to extract the fetal electrocardiogram. Clinical Physics and Physiological Measurement 1989,10(B):7-10. 10.1088/0143-0815/10/4B/001View ArticleGoogle Scholar
  8. De Lathauwer L, De Moor B, Vandewalle J: Fetal electrocardiogram extraction by blind source subspace separation. IEEE Transactions on Biomedical Engineering 2000,47(5):567-572. 10.1109/10.841326View ArticleGoogle Scholar
  9. Vrins F, Jutten C, Verleysen M: Sensor array and electrode selection for non-invasive fetal electrocardiogram extraction by independent component analysis. In Proceedings of 5th International Conference on Independent Component Analysis and Blind Signal Separation (ICA '04), September 2004, Granada, Spain, Lecture Notes in Computer Science Edited by: Puntonet CG, Prieto A. 3195: 1017-1014.View ArticleGoogle Scholar
  10. Azzerboni B, La Foresta F, Mammone N, Morabito FC: A new approach based on wavelet-ICA algorithms for fetal electrocardiogram extraction. Proceedings of 13th European Symposium on Artificial Neural Networks (ESANN '05), April 2005, Bruges, Belgium 193-198.Google Scholar
  11. Sameni R, Jutten C, Shamsollahi MB: What ICA provides for ECG processing: application to noninvasive fetal ECG extraction. Proceedings of the International Symposium on Signal Processing and Information Technology (ISSPIT '06), August 2006, Vancouver, Canada 656-661.Google Scholar
  12. Cichocki A, Amari S (Eds): Adaptive Blind Signal and Image Processing. John Wiley & Sons, New York, NY, USA; 2003.Google Scholar
  13. McSharry PE, Clifford GD, Tarassenko L, Smith LA: A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering 2003,50(3):289-294. 10.1109/TBME.2003.808805View ArticleGoogle Scholar
  14. McSharry PE, Clifford GD: ECGSYN - a realistic ECG waveform generator. http://www.physionet.org/physiotools/ecgsyn/
  15. Bergveld P, Meijer WJH: A new technique for the suppression of the MECG. IEEE Transactions on Biomedical Engineering 1981,28(4):348-354.View ArticleGoogle Scholar
  16. Meijer WJH, Bergveld P: The simulation of the abdominal MECG. IEEE Transactions on Biomedical Engineering 1981,28(4):354-357.View ArticleGoogle Scholar
  17. Geselowitz DB: On the theory of the electrocardiogram. Proceedings of the IEEE 1989,77(6):857-876. 10.1109/5.29327View ArticleGoogle Scholar
  18. Bronzino J (Ed): The Biomedical Engineering Handbook. 2nd edition. CRC Press, Boca Raton, Fla, USA; 2000.Google Scholar
  19. Frank E: An accurate, clinically practical system for spatial vectorcardiography. Circulation 1956,13(5):737-749.View ArticleGoogle Scholar
  20. Fletcher GF, Balady G, Froelicher VF, Hartley LH, Haskell WL, Pollock ML: Exercise standards: a statement for healthcare professionals from the American Heart Association. Circulation 1995,91(2):580-615.View ArticleGoogle Scholar
  21. Dower GE, Machado HB, Osborne JA: On deriving the electrocardiogram from vectorcardiographic leads. Clinical Cardiology 1980,3(2):87-95.Google Scholar
  22. Hadžievski L, Bojović B, Vukčević V, et al.: A novel mobile transtelephonic system with synthesized 12-lead ECG. IEEE Transactions on Information Technology in Biomedicine 2004,8(4):428-438. 10.1109/TITB.2004.837869View ArticleGoogle Scholar
  23. Edenbrandt L, Pahlm O: Vectorcardiogram synthesized from a 12-lead ECG: superiority of the inverse Dower matrix. Journal of Electrocardiology 1988,21(4):361-367. 10.1016/0022-0736(88)90113-6View ArticleGoogle Scholar
  24. Clifford GD, McSharry PE: A realistic coupled nonlinear artificial ECG, BP, and respiratory signal generator for assessing noise performance of biomedical signal processing algorithms. Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems II, May 2004, Maspalomas, Spain, Proceedings of SPIE 5467: 290-301.View ArticleGoogle Scholar
  25. Sameni R, Shamsollahi MB, Jutten C, Babaie-Zade M: Filtering noisy ECG signals using the extended Kalman filter based on a modified dynamic ECG model. Proceedings of the 32nd Annual International Conference on Computers in Cardiology, September 2005, Lyon, France 1017-1020.Google Scholar
  26. Clifford GD: A novel framework for signal representation and source separation: applications to filtering and segmentation of biosignals. Journal of Biological Systems 2006,14(2):169-183. 10.1142/S0218339006001830View ArticleMATHGoogle Scholar
  27. Ben-Arie J, Rao KR: Nonorthogonal signal representation by Gaussians and Gabor functions. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 1995,42(6):402-413. 10.1109/82.392315View ArticleMATHGoogle Scholar
  28. Fetal positions, WebMD http://www.webmd.com/content/tools/1/slide_fetal_pos.htm
  29. Clifford GD, Shoeb A, McSharry PE, Janz BA: Model-based filtering, compression and classification of the ECG. International Journal of Bioelectromagnetism 2005,7(1):158-161.Google Scholar
  30. Bishop C: Neural Networks for Pattern Recognition. Oxford University Press, New York, NY, USA; 1995.MATHGoogle Scholar
  31. Weixue L, Ling X: Computer simulation of epicardial potentials using a heart-torso model with realistic geometry. IEEE Transactions on Biomedical Engineering 1996,43(2):211-217. 10.1109/10.481990View ArticleGoogle Scholar
  32. Frenkel L, Feder M: Recursive expectation-maximization (EM) algorithms for time-varying parameters with applications to multiple target tracking. IEEE Transactions on Signal Processing 1999,47(2):306-320. 10.1109/78.740104View ArticleGoogle Scholar
  33. Sameni R, Shamsollahi MB, Jutten C, Clifford GD: A nonlinear Bayesian filtering framework for ECG denoising. to appear in IEEE Transactions on Biomedical EngineeringGoogle Scholar
  34. Friesen GM, Jannett TC, Jadallah MA, Yates SL, Quint SR, Nagle HT: A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Transactions on Biomedical Engineering 1990,37(1):85-98. 10.1109/10.43620View ArticleGoogle Scholar
  35. Moody G, Muldrow W, Mark R: Noise stress test for arrhythmia detectors. Proceedings of Annual International Conference on Computers in Cardiology, 1984, Salt Lake City, Utah, USA 381-384.Google Scholar
  36. Hu X, Nenov V: A single-lead ECG enhancement algorithm using a regularized data-driven filter. IEEE Transactions on Biomedical Engineering 2006,53(2):347-351. 10.1109/TBME.2005.862529View ArticleGoogle Scholar
  37. Gelb A (Ed): Applied Optimal Estimation. MIT Press, Cambridge, Mass, USA; 1974.Google Scholar
  38. Tarvainen MP, Georgiadis SD, Ranta-Aho PO, Karjalainen PA: Time-varying analysis of heart rate variability signals with a Kalman smoother algorithm. Physiological Measurement 2006,27(3):225-239. 10.1088/0967-3334/27/3/002View ArticleGoogle Scholar
  39. Moody G, Muldrow W, Mark R: The MIT-BIH noise stress test database. http://www.physionet.org/physiobank/database/nstdb/
  40. Härmä A: Frequency-warped autoregressive modeling and filtering, Doctoral thesis. 2001.Google Scholar
  41. The MIT-BIH PTB diagnosis database http://www.physionet.org/physiobank/database/ptbdb/
  42. Bousseljot R, Kreiseler D, Schnabel A: Nutzung der EKG-signaldatenbank CARDIODAT der PTB über das internet. Biomedizinische Technik 1995,40(1):S317-S318. 10.1515/bmte.1995.40.s1.317Google Scholar
  43. Kreiseler D, Bousseljot R: Automatisierte EKG-auswertung mit hilfe der EKG-signaldatenbank CARDIODAT der PTB. Biomedizinische Technik 1995,40(1):S319-S320. 10.1515/bmte.1995.40.s1.319Google Scholar
  44. De Moor B: Database for the identification of systems (DaISy). http://homes.esat.kuleuven.be/~smc/daisy/
  45. Taylor MJO, Smith MJ, Thomas M, et al.: Non-invasive fetal electrocardiography in singleton and multiple pregnancies. BJOG: An International Journal of Obstetrics and Gynaecology 2003,110(7):668-678. 10.1046/j.1471-0528.2003.02005.xView ArticleGoogle Scholar
  46. Cardoso J-F: Blind source separation and independent component analysis. http://www.tsi.enst.fr/~cardoso/guidesepsou.html
  47. Sameni R, Shamsollahi MB, Jutten C: Filtering electrocardiogram signals using the extended Kalman filter. Proceedings of the 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '05), September 2005, Shanghai, China 5639-5642.Google Scholar
  48. Gumbel EJ: Statistics of Extremes. Columbia University Press, New York, NY, USA; 1958.MATHGoogle Scholar
  49. Golub GH, van Loan CF: Matrix Computations. 3rd edition. Johns Hopkins University Press, Baltimore, Md, USA; 1996.MATHGoogle Scholar

Copyright

© Reza Sameni et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.