r. sameni , f. vrins , f. parmentier , c. hérail , v. vigneron ,
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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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)
(2) (1) (3)
MaxEnt 2006July 10th 2006, Paris, FRANCE
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Overview
Introduction Backgrounds Methods & Results Summary & Conclusions
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Overview
Introduction Backgrounds Methods & Results Summary & Conclusions
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Objective
The noninvasive extraction of fetal ECG (fECG) from an array of electrodes placed on the abdomen of a pregnant woman
Introduction
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Perspective:
Noninvasive Fetal ECG Extraction
Array Recorded Signals
Temporal Filtering
(Dynamic Bayesian Filter)
Spatial Filtering
(Blind Source Separation)
Introduction
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Perspective:
Noninvasive Fetal ECG Extraction
Array Recorded Signals
Temporal Filtering
(Dynamic Bayesian Filter)
Spatial Filtering
(Blind Source Separation)
Introduction
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The Array Recording System
Introduction
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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
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Why use ICA?
By using the array recordings we compensate the low fECG SNR by the spatial diversity of the electrodes
Introduction
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Problems with high-dimensional signals
Curse of dimensionality High processing cost Redundancy Sensitivity to noise Spurious components extracted by ICA
Introduction
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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
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Overview
Introduction Backgrounds Methods & Results Summary & Conclusions
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The electrical activity of the heart
The contraction of the heart muscle is due to the periodic stimulation of the cardiac nervous system.
Backgrounds
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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
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What is the ECG?
The Electrocardiogram (ECG) is the overall electrical activity of the heart recorded from the body surface
Backgrounds
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What is the Vectorcardiogram?
The Vectorcardiogram (VCG) is a 3D representation of 3 orthogonal ECG leads
Backgrounds
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A dynamic model for the generation of synthetic maternal abdominal signals
Backgrounds
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Overview
Introduction Backgrounds Methods & Results Summary & Conclusions
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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
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Typical Signals Extracted by ICA
Maternal ECG
Noise
Systematic Noise
Fetal ECG
Methods & Results
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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
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MI results on simulated data
Methods & Results
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Mutual Information (MI)
F and G are Invertible Transformations
X and Y can be either scalars or vectors
Methods & Results
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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
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Previous sensor selection strategy
Rejection of the channels with the most MI with the maternal ECG:
( , )refI X mECGMaternal reference
Methods & Results
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Typical ECG recordings
Methods & Results
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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
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1. Classification of electrodes based on the maternal contribution:
Methods & Results
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2-1. Ranking of electrodes based on the maternal contribution:(Rule #1)
Methods & Results
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2-2. Ranking of electrodes based on the maternal contribution:(Rule #2)
Methods & Results
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3. Ranking of electrodes based on the fetal contribution:
Methods & Results
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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
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Overview
Introduction Backgrounds Methods & Results Summary & Conclusions
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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
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