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

In this paper, 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 article may serve as an effective means of simulation and analysis of a wide range of ECGs, including adults and fetuses.

[1]  E. Frank An Accurate, Clinically Practical System For Spatial Vectorcardiography , 1956, Circulation.

[2]  Jezekiel Ben-Arie,et al.  Nonorthogonal signal representation by Gaussians and Gabor functions , 1995 .

[3]  D. Kreiseler,et al.  Automatisierte EKG-Auswertung mit Hilfe der EKG-Signaldatenbank CARDIODAT der PTB , 1995 .

[4]  Patrick E. McSharry,et al.  A dynamical model for generating synthetic electrocardiogram signals , 2003, IEEE Transactions on Biomedical Engineering.

[5]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[6]  O. Pahlm,et al.  Vectorcardiogram synthesized from a 12-lead ECG: superiority of the inverse Dower matrix. , 1988, Journal of electrocardiology.

[7]  N. Fisk,et al.  Non‐invasive fetal electrocardiography in singleton and multiple pregnancies , 2003, BJOG : an international journal of obstetrics and gynaecology.

[8]  R. Granit THE HEART ( Extract from “ Principles and Applications of Bioelectric and Biomagnetic Fields , 2005 .

[9]  Francesco Carlo Morabito,et al.  A new approach based on wavelet-ICA algorithms for fetal electrocardiogram extraction , 2005, ESANN.

[10]  Meir Feder,et al.  Recursive expectation-maximization (EM) algorithms for time-varying parameters with applications to multiple target tracking , 1999, IEEE Trans. Signal Process..

[11]  J Janssens,et al.  Description of a real-time system to extract the fetal electrocardiogram. , 1989, Clinical physics and physiological measurement : an official journal of the Hospital Physicists' Association, Deutsche Gesellschaft fur Medizinische Physik and the European Federation of Organisations for Medical Physics.

[12]  Roger G. Mark,et al.  The MIT-BIH Noise Stress Test Database , 1992 .

[13]  Ljupco Hadzievski,et al.  A novel mobile transtelephonic system with synthesized 12-lead ECG , 2004, IEEE Transactions on Information Technology in Biomedicine.

[14]  Michel Verleysen,et al.  Sensor Array and Electrode Selection for Non-invasive Fetal Electrocardiogram Extraction by Independent Component Analysis , 2004, ICA.

[15]  G. Clifford A NOVEL FRAMEWORK FOR SIGNAL REPRESENTATION AND SOURCE SEPARATION: APPLICATIONS TO FILTERING AND SEGMENTATION OF BIOSIGNALS , 2006 .

[16]  Patrick E. McSharry,et al.  A realistic coupled nonlinear artificial ECG, BP, and respiratory signal generator for assessing noise performance of biomedical signal processing algorithms , 2004, SPIE International Symposium on Fluctuations and Noise.

[17]  David B. Geselowitz,et al.  On the theory of the electrocardiogram , 1989, Proc. IEEE.

[18]  Piet Bergveld,et al.  A New Technique for the Suppression of the MECG , 1981, IEEE Transactions on Biomedical Engineering.

[19]  C. Jutten,et al.  What ICA Provides for ECG Processing: Application to Noninvasive Fetal ECG Extraction , 2006, 2006 IEEE International Symposium on Signal Processing and Information Technology.

[20]  Elmar Wolfgang Lang,et al.  Blind source separation and independent component analysis , 2006, Neurocomputing.

[21]  Andrzej Cichocki,et al.  Adaptive blind signal and image processing , 2002 .

[22]  Joos Vandewalle,et al.  Fetal electrocardiogram extraction by blind source subspace separation , 2000, IEEE Transactions on Biomedical Engineering.

[23]  Xiao Hu,et al.  A single-lead ECG enhancement algorithm using a regularized data-driven filter , 2006, IEEE Transactions on Biomedical Engineering.

[24]  H. T. Nagle,et al.  A comparison of the noise sensitivity of nine QRS detection algorithms , 1990, IEEE Transactions on Biomedical Engineering.

[25]  C. Jutten,et al.  Filtering noisy ECG signals using the extended kalman filter based on a modified dynamic ECG model , 2005, Computers in Cardiology, 2005.

[26]  G. Dower,et al.  On Deriving the Electrocardiogram from Vectorcardiographic Leads , 1980, Clinical cardiology.

[27]  Christian Jutten,et al.  A Nonlinear Bayesian Filtering Framework for ECG Denoising , 2007, IEEE Transactions on Biomedical Engineering.

[28]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[29]  V. Froelicher,et al.  Exercise standards. A statement for healthcare professionals from the American Heart Association. Writing Group. , 1995, Circulation.

[30]  Ali H. Shoeb,et al.  Model-based filtering, compression and classification of the ECG , 2005 .

[31]  Joseph D. Bronzino,et al.  The Biomedical Engineering Handbook , 1995 .

[32]  Aki Härmä,et al.  Frequency-warped autoregressive modeling and filtering , 2001 .

[33]  Piet Bergveld,et al.  The Simulation of the Abdominal MECG , 1981, IEEE Transactions on Biomedical Engineering.

[34]  Ee-Chien Chang,et al.  Blind separation of fetal ECG from single mixture using SVD and ICA , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[35]  Ralf Bousseljot,et al.  Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet , 2009 .

[36]  G. Saha,et al.  Fetal ECG extraction from single-channel maternal ECG using singular value decomposition , 1997, IEEE Transactions on Biomedical Engineering.

[37]  L. Weixue,et al.  Computer simulation of epicardial potentials using a heart-torso model with realistic geometry. , 1996, IEEE transactions on bio-medical engineering.

[38]  M. Tarvainen,et al.  Time-varying analysis of heart rate variability signals with a Kalman smoother algorithm , 2006, Physiological measurement.

[39]  A.H. Haddad,et al.  Applied optimal estimation , 1976, Proceedings of the IEEE.

[40]  C. Jutten,et al.  Filtering Electrocardiogram Signals Using the Extended Kalman Filter , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.