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Real-time identification of cardiac substrate anomalies

Real-time identification of cardiac substrate anomalies. Author : Philippe Haldermans Promoters : dr. Ronald Westra dr. ir. Ralf Peeters. 13th September 2004. Contents. Motivation Forward modelling Inverse methods Results Conclusions. Motivation. Atrium fibrillation (AF)

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Real-time identification of cardiac substrate anomalies

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  1. Real-time identification of cardiac substrate anomalies Author : Philippe Haldermans Promoters : dr. Ronald Westra dr. ir. Ralf Peeters 13th September 2004

  2. Contents • Motivation • Forward modelling • Inverse methods • Results • Conclusions

  3. Motivation • Atrium fibrillation (AF) • cell triggers • wave maintenance by substrate anomalies • New spatial-temporal data  better image of wave propagation (movie)

  4. Objective

  5. Forward modelling (1) • Biophysically detailed models + Luo-Rudy, Beeler-Reuter, … • Complicated for inverse method • Cellular automata + Simple and fast, especially for normal propagation • Absence of parameters for inverse estimation

  6. Forward modelling (2) • Fitzhugh-Nagumo model • Partial differential equation

  7. Forward modelling (3) • Discretized in time and space • Space : symmetric estimation • Time : normal estimation

  8. Experiments (1) • Types of waves: • Planar • Spherical • Spiral • Different sorts of tissue: • Isotropic Anisotropic • Homogeneous Inhomogeneous

  9. Experiments (2) • Refractory period • Re-entering waves • Spiral waves (spiral.avi) • Figure-8 reentry (figure8.avi) • Laws of physics • Rotations • Snellius’ law

  10. Inverse methods • Rewriting equations  linear in the parameters • Iterative linear least squares estimation • Proof of usefulness • Robustness for rounding errors • Effect of noisy data

  11. Results (1) • Simulated data: • Good estimation of the parameters • Method holds even with noisy data • Able to find anomalies (tissue) (demo) • Data movies • Proved in theory  estimation works • Practical problems with matlab

  12. Results (2) • Real data : • First dataset (movie) • shows normal propagation • method finds smooth surface (tissue) • Second dataset (movie) • fibrillatory propagation • no anomalies in the conductivity (tissue) • example of other problem : cell triggering?

  13. Other inverse methods (1) • Bayesian approach • estimation of the uncertainty • groups of solutions • prior distribution & likelihood function  posterior distribution • can be used as first estimation for other methods

  14. Other inverse methods (2) • Regularization • Moore-Penrose pseudo-inverse • Problems with : • Small singular values + noisy data • Possible solutions : • Truncated singular value decomposition • Tikhonov regularization

  15. Conclusions • Identify spatial anomalies in the conductivity • Fitzhugh-Nagumo  Realistic properties • Estimation method works + is robust • Real data • able to give conductivity • these examples show no problems in the conductivity

  16. Recommendations (1) • Other forward model • Biologically more detailled • Other properties • Different inverse method • Bayesian, regularization, … • Combination: least squares with Bayesian

  17. Recommendations (2) • Real data • More datasets • More information about the data • Combination with the spatial-temporal data measurement  real-time identification

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