R. Sameni , F. Vrins , F. Parmentier , C. Hérail , V. Vigneron ,

1. Electrode Selection for Noninvasive Fetal Electrocardiogram Extraction using Mutual Information Criteria R. Sameni , F. Vrins , F. Parmentier , C. Hérail , V. Vigneron , M. Verleysen , C. Jutten , and M. B. Shamsollahi (1) Laboratoire des Images et des Signaux (LIS) – CNRS UMR 5083, INPG, UJF, Grenoble, France (2) Machine Learning Group (MLG), Microelectronics Laboratory, Université Catholique de Louvain (UCL), Louvain-La-Neuve, Belgium (3) Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, Tehran, Iran (4) Laboratoire Systèmes Complexes (LSC) – CNRS FRE 2494, Evry, France (1,3) (2) (2) (4) (4) (1) (3) (2) MaxEnt 2006 July 10th 2006, Paris, FRANCE

2. Overview • Introduction • Backgrounds • Methods & Results • Summary & Conclusions

3. Overview • Introduction • Backgrounds • Methods & Results • Summary & Conclusions

4. Introduction Objective • The noninvasive extraction of fetal ECG (fECG) from an array of electrodes placed on the abdomen of a pregnant woman

5. Introduction Perspective: Array Recorded Signals Spatial Filtering (Blind Source Separation) Temporal Filtering (Dynamic Bayesian Filter) Noninvasive Fetal ECG Extraction

6. Introduction Perspective: Array Recorded Signals Spatial Filtering (Blind Source Separation) Temporal Filtering (Dynamic Bayesian Filter) Noninvasive Fetal ECG Extraction

7. Introduction The Array Recording System

8. Introduction Challenging issues in fECG extraction • No direct access to the fetus • Weakness of the fECG • Maternal ECG, EMG, Diaphragm, and Uterus noises • Attenuation of the fECG in the maternal body • Fetal movement and rotation • Necessity of a canonical fECG representation • fECG of twins and triplings • … Noninvasive fECG extraction is a challenging application for the ICA community

9. Introduction Why use ICA? • By using the array recordings we compensate the low fECG SNR by the spatial diversity of the electrodes

10. Introduction Problems with high-dimensional signals • Curse of dimensionality • High processing cost • Redundancy • Sensitivity to noise • Spurious components extracted by ICA

11. Introduction General Perspective • Record high-dimensional data • Select the channels containing the most information about the fetal heart • Extract the fetal components using ICA (a canonical representation of the fetal ECG) • Dynamically re-select the channels according to the fetal movements

12. Overview • Introduction • Backgrounds • Methods & Results • Summary & Conclusions

13. Backgrounds The electrical activity of the heart • The contraction of the heart muscle is due to the periodic stimulation of the cardiac nervous system.

14. Backgrounds The electrical activity of the heart • Single dipole model: A rotating time-variant vector located at the heart. • Other Models:Moving dipole, Multipole, Activation maps, …

15. Backgrounds What is the ECG? • The Electrocardiogram (ECG) is the overall electrical activity of the heart recorded from the body surface

16. Backgrounds What is the Vectorcardiogram? • The Vectorcardiogram (VCG) is a 3D representation of 3 orthogonal ECG leads

17. Backgrounds A dynamic model for the generation of synthetic maternal abdominal signals

18. Overview • Introduction • Backgrounds • Methods & Results • Summary & Conclusions

19. Methods & Results Channel selection vs. projection • The fECG components are very weak, and will be removed by projection • For noisy signals, ICA can artificially extract signals which do not correspond to any physiological source

20. Methods & Results Typical Signals Extracted by ICA Maternal ECG Fetal ECG Noise Systematic Noise

21. Methods & Results Which measure of selection? • We require a measure for the selection of the most- and least- informative leads. • As we use the channel selection as a preprocessing for ICA the Mutual Information (MI) between each lead and the maternal and fetal components is a reasonable candidate.

22. Methods & Results MI results on simulated data

23. Methods & Results Mutual Information (MI) X and Y can be either scalars or vectors F and G are Invertible Transformations

24. Methods & Results Mutual Information for ECG and VCG signals Result: The MI calculated between any body surface recording and the VCG signals is ‘rather’ robust to the locations of the VCG electrodes

25. Methods & Results Previous sensor selection strategy • Rejection of the channels with the most MI with the maternal ECG: Maternal reference

26. Methods & Results Typical ECG recordings

27. Methods & Results New Channel Selection Strategy • A three step selection with multiple reference channels: • Classification of the electrodes according to their correlation with the maternal ECG • Rejecting the channels with the most MI with the maternal ECG • Among the remaining channels, keeping the ones with the most MI with the fetal ECG

28. Methods & Results 1. Classification of electrodes based on the maternal contribution:

29. Methods & Results 2-1. Ranking of electrodes based on the maternal contribution:(Rule #1)

30. Methods & Results 2-2. Ranking of electrodes based on the maternal contribution:(Rule #2)

31. Methods & Results 3. Ranking of electrodes based on the fetal contribution:

32. Methods & Results Typical fECG signals extracted from by using the electrode selection rules fECG extracted from 20 selected leads fECG extracted from the whole data set fECG extracted from 10 selected leads

33. Overview • Introduction • Backgrounds • Methods & Results • Summary & Conclusions

34. Summary & Conclusions Summary & Conclusions: • We proposed a channel selection algorithm for the selection of the most informative sensors corresponding to the fetal ECG signals • By using the MI with appropriate models for the heart signals we can effectively reduce the number of channels with minimal loss of information

35. Thanks for your attention!