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 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

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] • 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

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 • 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

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 (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

17. Experiments setup

18. Experiments setup

19. Experiment Phases

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

33. Back Subject 1QRS component 2 Chest

34. Back Subject 1QRS component 3 Chest

35. Back Subject 1QRS component 4 Chest

36. Back Subject 1QRS component 5 Chest

37. Back Subject 1QRS component 6 Chest

38. Back Subject 1T-wave component Chest

39. Subject 2P-wave map

40. Subject 2QRS component 1

41. Back QRS component 2 Chest

42. Subject 2QRS component 3

43. Back Subject 2QRS component 4 Chest

44. Subject 2T-wave component 1

45. Back Subject 2T-wave component 2 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, 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