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

Overview

Introduction Backgrounds Methods & Results Summary & Conclusions

Overview

Introduction Backgrounds Methods & Results Summary & Conclusions

Objective

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

Introduction

Perspective:

Noninvasive Fetal ECG Extraction

Array Recorded Signals

Temporal Filtering

(Dynamic Bayesian Filter)

Spatial Filtering

(Blind Source Separation)

Introduction

Perspective:

Noninvasive Fetal ECG Extraction

Array Recorded Signals

Temporal Filtering

(Dynamic Bayesian Filter)

Spatial Filtering

(Blind Source Separation)

Introduction

The Array Recording System

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

Introduction

Why use ICA?

By using the array recordings we compensate the low fECG SNR by the spatial diversity of the electrodes

Introduction

Problems with high-dimensional signals

Curse of dimensionality High processing cost Redundancy Sensitivity to noise Spurious components extracted by ICA

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

Introduction

Overview

Introduction Backgrounds Methods & Results Summary & Conclusions

The electrical activity of the heart

The contraction of the heart muscle is due to the periodic stimulation of the cardiac nervous system.

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, …

Backgrounds

What is the ECG?

The Electrocardiogram (ECG) is the overall electrical activity of the heart recorded from the body surface

Backgrounds

What is the Vectorcardiogram?

The Vectorcardiogram (VCG) is a 3D representation of 3 orthogonal ECG leads

Backgrounds

A dynamic model for the generation of synthetic maternal abdominal signals

Backgrounds

Overview

Introduction Backgrounds Methods & Results Summary & Conclusions

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

Typical Signals Extracted by ICA

Maternal ECG

Noise

Systematic Noise

Fetal ECG

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.

Methods & Results

MI results on simulated data

Methods & Results

Mutual Information (MI)

F and G are Invertible Transformations

X and Y can be either scalars or vectors

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

Methods & Results

Previous sensor selection strategy

Rejection of the channels with the most MI with the maternal ECG:

( , )refI X mECGMaternal reference

Methods & Results

Typical ECG recordings

Methods & Results

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

1. Classification of electrodes based on the maternal contribution:

Methods & Results

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

Methods & Results

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

Methods & Results

3. Ranking of electrodes based on the fetal contribution:

Methods & Results

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

Overview

Introduction Backgrounds Methods & Results 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

Summary & Conclusions

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