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) (4) (2) (1)(3) MaxEnt 2006 July 10 th 2006, Paris, FRANCE

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

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

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

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

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

7. The Array Recording System Introduction

8. 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 Introduction

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

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

11. 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 Introduction

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

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

14. 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, … Backgrounds

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

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

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

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

19. 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 Methods & Results

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

21. 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. Methods & Results

22. MI results on simulated data Methods & Results

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

24. 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 Methods & Results

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

26. Typical ECG recordings Methods & Results

27. New Channel Selection Strategy  A three step selection with multiple reference channels: 1.Classification of the electrodes according to their correlation with the maternal ECG 2.Rejecting the channels with the most MI with the maternal ECG 3.Among the remaining channels, keeping the ones with the most MI with the fetal ECG Methods & Results

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

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

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

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

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

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

34. 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 Summary & Conclusions

35. Thanks for your attention!