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
• University of California, San Diego
• Oct 18, 2006

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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

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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

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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

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ICA definition

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

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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

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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

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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)

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Gradient descent approach

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

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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

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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

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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

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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

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Experiments setup
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Experiments setup
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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)
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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

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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

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Recorded waves for subject 1 (Action I - standing)
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Recorded waves for subject 1 (Action III - horse
stance)
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Recorded waves for subject 2 (Action I - standing)
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Recorded waves for subject 2 (Action III - horse
stance)
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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

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Separated components for subject 1
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Separated components for subject 2
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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

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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

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Illustration of electrodes locations
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Subject 1QRS component 1
Back
Chest
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Subject 1QRS component 2
Back
Chest
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Subject 1QRS component 3
Back
Chest
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Subject 1QRS component 4
Back
Chest
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Subject 1QRS component 5
Back
Chest
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Subject 1QRS component 6
Back
Chest
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Subject 1T-wave component
Back
Chest
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Subject 2P-wave map
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Subject 2QRS component 1
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QRS component 2
Back
Chest
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Subject 2QRS component 3
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Subject 2QRS component 4
Back
Chest
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Subject 2T-wave component 1
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Subject 2T-wave component 2
Back
Chest
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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

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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

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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