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Noninvasive Study of the Human Heart using Independent Component Analysis

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Noninvasive Study of the Human Heart using Independent Component Analysis Y. Zhu, T-L Chen, W. Zhang, T-P Jung, J-R Duann, S. Makeig and C-K Cheng – PowerPoint PPT presentation

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Title: Noninvasive Study of the Human Heart using Independent Component Analysis


1
Noninvasive Study of the Human Heart using
Independent Component Analysis
  • Y. Zhu, T-L Chen, W. Zhang, T-P Jung,
  • J-R Duann, S. Makeig and C-K Cheng
  • University of California, San Diego
  • Oct 18, 2006

2
Outline
  • Background
  • Independent Component Analysis
  • Experiments
  • Equipments Procedures
  • Results components, back projection maps
  • Summary Future Work

3
Background
  • Objective of heart simulation
  • Diagnose heart diseases efficiently
  • Help doctors easily locate the problem
  • Advantage of noninvasive measurement
  • More cost effective
  • Much simpler and faster to prepare, setup and
    take measurements

4
12-lead ECG shortcomings
  • Too few information to separate different sources
  • A heart disease may be caused by multiple
    conditions
  • E.g. myocardial infarction may happen in multiple
    locations
  • Need more channels to detect ECG waveforms

5
Contributions
  • Design noninvasive experiments to collect heart
    signals from around 100 channels
  • Analyze the data using Independent Component
    Analysis (ICA)
  • Successfully identify different components of
    P-wave, QRS-complex and T-wave

6
Previous works on ICA
  • Originally proposed by to solve blind source
    separation problem by Camon 1 in 1994
  • Gained more attraction and popularity from Bell
    and Sejnowskis infomax principle 2
  • Jung et al. applied ICA to ECG, EEG, MEG and fMRI
    34
  • Separate maternal and fetal heart beats and
    remove artifacts

7
ICA definition
  • N source signals s s1,s2,,sN linearly
    mixed x x1,x2,,xN As
  • If x is known, recover sources as u Wx
  • u is only different from s in scaling and
    permutation

8
ICA definition
  • Objective is to find a square matrix W
  • Key assumption the source signals are
    statistically independent

9
ICA definition
  • Joint probability the probability of two or more
    things happening together
  • Statistical independence the joint probability
    density function (pdf) can be factorized to the
    product of individual probabilities of each
    source

10
ICA algorithms
  • Gradient descent by infomax principle 2
  • Hyvariens FastICA 2
  • Cardosos 4th-order algorithms JADE 56
  • Many others 7
  • They may produce difference solutions and the
    significance is hard to measure

11
Gradient descent approach
  • Has been proven to effective in analyzing
    biomedical signals
  • Objective is to minimize the redundancy
  • Equivalent to maximizing the joint entropy of the
    cumulative density function (cdf)

12
Gradient descent approach
  • W can be updated using the following iterative
    equation
    (cdf) (entropy)
  • learning rate

13
Gradient descent approach
  • W is first initialized to the identity matrix and
    iteratively updated until the change is
    sufficiently small
  • Main Parameters when using the package
  • Learning rate 10-4
  • Stopping threshold 10-7
  • Maximum steps 103

14
Experiments equipments
  • BioSemis ActiveTwo Base system
  • Main components
  • 4x32 pin-type active electrodes
  • Collecting signals and remove common mode noise
    in real time
  • 128 electrode holders
  • Fix the electrodes
  • Electrode gel
  • Conductor between electrodes and skin
  • Adhesive pads
  • Fix the holders on skin
  • 16x8 channel amplifier/converter modules
  • LabView Software
  • ICA Package EEGLAB

15
Experiments setup prodcures
  • 1. Attach electrode holders to the skin by
    adhesive pads, forming two identical matrices on
    the chest and back
  • 2. Inject gel in the holders
  • 3. Plug in electrodes

16
Experiments setup procedures (contd)
  • 4. Place 3 electrodes on the left arm, right arm
    and left leg as the unipolar limb leads and place
    the electrodes CMS/DRL on the waist as the
    grounding electrodes
  • Connect electrodes to the AD-box

17
Experiments setup
18
Experiments setup
19
Experiment Phases
Actions Description
Action I Stand and breath normally
Action II Breath and hold breath for intervals of 10 seconds
Action III Hold horse stance for a certain period and record after that
Action IV Lean to forward, backward, left and right (4 poses)
20
Purposes for multiple phases
  • Create different conditions so that different
    waveforms can be generated
  • The distances between P-wave, QRS complex and
    T-wave vary in different circumstance
  • Enable ICA algorithm to separate them

21
Characteristics of recorded waves
  • The electrodes on the chest receive much stronger
    signals
  • Heart is closer to the front
  • Waves in different activities have different
    characteristics
  • Heart beat rates
  • Shapes of QRS complexes and T-waves

22
Recorded waves for subject 1 (Action I - standing)
23
Recorded waves for subject 1 (Action III - horse
stance)
24
Recorded waves for subject 2 (Action I - standing)
25
Recorded waves for subject 2 (Action III - horse
stance)
26
Characteristics of ICA results
  • QRS complex and T-wave can be clearly separated
    for subject 1
  • P-wave, QRS complex and T-wave can be clearly
    separated for subject 2
  • QRS complex is decomposed into several components
    with different peak time
  • Maybe a sequence of wave propagation
  • Multiple activities are essential to perform ICA
    successfully
  • At least 3, more are better

27
Separated components for subject 1
28
Separated components for subject 2
29
Back projection
  • W is obtained unmixing matrix, is
    mixing matrix
  • The i-th column of represents the
    weight of each channel that contributes to the
    i-th decomposed component
  • According to physical location of each channel,
    we can plot potential maps for each component

30
Characteristics of back projection maps
  • Weights are concentrated in the left part of the
    front chest
  • P-wave source occupies upper portion
  • Sources are moving downward from QRS components
    to T-waves
  • Estimate the dipoles according to the maps from
    the most negative to most positive locations

31
Illustration of electrodes locations
32
Subject 1 QRS component 1
Back
Chest
33
Subject 1 QRS component 2
Back
Chest
34
Subject 1 QRS component 3
Back
Chest
35
Subject 1 QRS component 4
Back
Chest
36
Subject 1 QRS component 5
Back
Chest
37
Subject 1 QRS component 6
Back
Chest
38
Subject 1 T-wave component
Back
Chest
39
Subject 2 P-wave map
40
Subject 2 QRS component 1
41
QRS component 2
Back
Chest
42
Subject 2 QRS component 3
43
Subject 2 QRS component 4
Back
Chest
44
Subject 2 T-wave component 1
45
Subject 2 T-wave component 2
Back
Chest
46
Summary
  • Design experiments to collect stable heart
    signals from multiple channels for analysis
  • Apply ICA techniques to find out meaningful heart
    wave components
  • Plot back projection maps to discover the
    properties of each component

47
Future work
  • Experiment on more subjects
  • Calculate wave propagation speed according to the
    QRS components verify the consistency with
    physiological observations
  • Seek for better ICA algorithms with the
    consideration on heart wave characteristics

48
References
  • 1 P. Camon. Independent component analaysis, a
    new concept? Signal Processing, 36287-314, 1994
  • 2 A. Hyvaerinen, J. Karhunen and E. Oja.
    Independent Component Analysis. John Wiley
    Sons, Inc. 2001
  • 3 T.P. Jung et al. Independent component
    analysis of biomedical signals. In 2nd
    International Workshop on Independent Component
    Analysis and Signal Separation
  • 4 T.P. Jung et al. Imaging brain dynamics using
    independent component analysis. Proceeding of the
    IEEE, 89(7), 2001
  • 5 J. Cardoso and A. Soloumiac. Blind
    beamforming for non-gaussian signals. IEE
    proceedings, 140(46)362-370, 1993
  • 6 J. Cardoso. High-order contrasts for
    independent component anlysis. Neural
    Computation, 11(1)157-192, 1999
  • 7 A. Hyvarinen. Survey on independent component
    analysis. Neural Computation Survey, 294-128,
    1999
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