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

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

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

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

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

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 Sejnowski’s infomax principle [2] Jung et al. applied ICA to ECG, EEG, MEG and fMRI [3][4] Separate maternal and fetal heart beats and remove artifacts

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

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

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

ICA algorithms Gradient descent by infomax principle [2] Hyvarien’s FastICA [2] Cardoso’s 4th-order algorithms JADE [5][6] Many others [7] They may produce difference solutions and the significance is hard to measure

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)

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

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

Experiments equipments BioSemi’s 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

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

Experiments setup procedures (cont’d) 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

Experiments setup

Experiments setup

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)

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

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

Recorded waves for subject 1 (Action I - standing)

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

Recorded waves for subject 2 (Action I - standing)

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

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

Separated components for subject 1

Separated components for subject 2

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

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

Illustration of electrodes locations

Back Subject 1 QRS component 1 Chest Subject right left Chest Back

Back Subject 1 QRS component 2 Chest

Back Subject 1 QRS component 3 Chest

Back Subject 1 QRS component 4 Chest

Back Subject 1 QRS component 5 Chest

Back Subject 1 QRS component 6 Chest

Subject 1 T-wave component Back Subject 1 T-wave component Chest

Subject 2 P-wave map

Subject 2 QRS component 1

Back QRS component 2 Chest

Subject 2 QRS component 3

Back Subject 2 QRS component 4 Chest

Subject 2 T-wave component 1

Subject 2 T-wave component 2 Back Subject 2 T-wave component 2 Chest

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

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

References [1] P. Camon. Independent component analaysis, a new concept? Signal Processing, 36:287-314, 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, 2:94-128, 1999