logging in or signing up NWO 2004 Charlie Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 155 Category: Education License: All Rights Reserved Like it (0) Dislike it (0) Added: June 18, 2007 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Slide1: Ronald L. Westra Department of Mathematics Maastricht University Complex Pattern Formation in Electrophysiological Wave Propagation on Cardiac Walls Slide2: Problem formulation Outline of proposed research Empirical observations and experimental equipment Theoretical framework and complex behavior Spatiotemporal analysis with wavelets Multidisciplinary approach Relation to other research Items in this Presentation Slide3: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Problem formulation Slide4: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Answer: yes, if the substrate is damaged Problem formulation Slide5: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Answer: yes, if the substrate is damaged Question 2: What about complex patterns if the substrate is not damaged? Problem formulation Slide6: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Answer: yes, if the substrate is damaged Question 2: What about complex patterns if the substrate is not damaged? Q3: In what language should we characterize these patterns? Problem formulation Slide7: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Answer: yes, if the substrate is damaged Question 2: What about complex patterns if the substrate is not damaged? Q3: In what language should we characterize these patterns? Q4: How to associate them with pathological conditions? Problem formulation Slide8: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Answer: yes, if the substrate is damaged Question 2: What about complex patterns if the substrate is not damaged? Q3: In what language should we characterize these patterns? Q4: How to associate them with pathological conditions? Q5: What are the driving parameters and their critical values? Problem formulation Slide9: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Answer: yes, if the substrate is damaged Question 2: What about complex patterns if the substrate is not damaged? Q3: In what language should we characterize these patterns? Q4: How to associate them with pathological conditions? Q5: What are the driving parameters and their critical values? Problem formulation Q6: Can the dynamical systems approach and wavelet analysis be useful? Slide10: * Mathematical framework for modelling of electrophysiological wave propagation and analysis of the resulting patterns * Analysis of annotated empirical spatiotemporal data * Identification of abnormal substrate * Dynamic analysis of complex behavior Outline of proposed research Slide11: Spatiotemporal annotated data from in-vivo experiments Dynamical systems approach Fractal wavelet analysis Identification techniques Methodology andamp; approach Slide12: Background: 1D-pattern analysis The ECG with the characteristic P,Q,R,S,T components (van Einthoven, 1936) Slide13: 1D-Morphological analysis : Traditionally, ECG-signal analysis is focussed on the single R-wave event signaling the occurrence of cardiac depolarization, morphological attributes of the electrogram are not taken into account. Slide14: 1D-Morphological analysis : Relation between the shape of the part and possible cardiopathologies Slide15: 1D-Morphological analysis : Wavelets are superior in identifying the characteristic parts of the signal. Slide16: Direct observation of electrophysiological waves on cardiac walls, using MRI or array of electrodes Regular behavior in case of normal substrate and normal pacing Complex behavior in case of damaged substrate , fast pacing (Wenkebach), and e.g. some types of atrium fibrilation (afib III) 2D-spatiotemporal pattern analysis ‘Spoon’ of about 3 cm2 with matrix ofelectrodes with grid spacing 0.2 mm and sampling frequency of > 1 kHz: ‘Spoon’ of about 3 cm2 with matrix of electrodes with grid spacing 0.2 mm and sampling frequency of andgt; 1 kHz 2D-spatiotemporal pattern analysis Spoon is positioned on the cardiac wall: Spoon is positioned on the cardiac wall 2D-spatiotemporal pattern analysis Electrostatic potential is measured: Electrostatic potential is measured 2D-spatiotemporal pattern analysis electrostatic field fractal wave type Normal: Normal Empirical Observations Regular, quasi-periodic, soliton-like wave fronts Atrium fibrilation type III: Atrium fibrilation type III Empirical Observations ‘Figure 8’ re-entry ‘spiral-wave’ re-entry Wave annihilation * The electrostatic field propagates in soliton-like, well-localized shockfront waves* A normal heart exhibits regular quasi-periodic waves seemingly without dispersion or dissipation* Damaged substrate causes irregular behavior like re-entry phenomena * Re-entry phenomena can also be caused by refractory substrate* Other complex patterns like wave-annihilation or breathers not-related to substrate* Some parameters drive complex behavior like period of sinus rhythm and the Wenkebach-effect : * The electrostatic field propagates in soliton-like, well-localized shockfront waves * A normal heart exhibits regular quasi-periodic waves seemingly without dispersion or dissipation * Damaged substrate causes irregular behavior like re-entry phenomena * Re-entry phenomena can also be caused by refractory substrate * Other complex patterns like wave-annihilation or breathers not-related to substrate * Some parameters drive complex behavior like period of sinus rhythm and the Wenkebach-effect Empirical Observations Slide23: Propagation of electrostatic potential over cardiac substrate * substrate properties: capacity , conductivity , and parameters * diffusion-like equation causing dispersion and exponential extinction * extinction is prevented by active ion-currents exhibiting a refractory period in substrate Microscopic Equations Propagation of electrophysiological waves [1]: electrostatic potential scalar capacity density scalar 1st order approximation conductivity tensor 1st order approximation (Ohm) model parameter vectors Propagation of electrophysiological waves [1] Propagation of electrophysiological waves [2]: food supply/inhibition scalar Ion reaction currents Phenomenological models Propagation of electrophysiological waves [2] Propagation of electrophysiological waves [3]: * Ion reaction current involves many terms: * These terms are individually modeled empirically by piecewise-linear equations * Iion can also be modeled phenomenologically, as by Luo-Rudy and Fitzhugh-Nagumo, e.g. : * The food supply/inhibition scalar f can also be phenomenological entity, e.g. Fitzhugh-Nagumo: Propagation of electrophysiological waves [3] Slide27: Parametrized PDE models like Fitzhugh-Nagumo allow for the identification of damaged substrate from empirical spatiotemporal data using e.g. nonlinear least-squares methods Westra R.L., Haldermans Ph., Peeters R.L.M., 2004 Identification of damaged substrate Normal: data substrate identified from data: Identification of damaged substrate Normal: data substrate identified from data Afib III: data substrate identified from data: Identification of damaged substrate Afib III: data substrate identified from data Complex patterns need not associate with damaged substrate:Hoekstra (2000) calls atrium fibrillation a ‘dynamical disease’Aliev, Panfilov (1998): period of sinus rhythm drives the Wenkebach-effect, Sidorov, Aliev, et al. (2003), Spatiotemporal Dynamics of Damped Propagation in Excitable Cardiac Tissue, Phys. Rev. Letters 2003Gray RA, et al., Nonstationary vortexlike reentrant activity as a mechanism of polymorphic ventricular tachycardia. Circulation 1995: Complex patterns need not associate with damaged substrate: Hoekstra (2000) calls atrium fibrillation a ‘dynamical disease’ Aliev, Panfilov (1998): period of sinus rhythm drives the Wenkebach-effect, Sidorov, Aliev, et al. (2003), Spatiotemporal Dynamics of Damped Propagation in Excitable Cardiac Tissue, Phys. Rev. Letters 2003 Gray RA, et al., Nonstationary vortexlike reentrant activity as a mechanism of polymorphic ventricular tachycardia. Circulation 1995 Pattern Formation and the Emergence of Complexity Propagation models like Fitzhugh-Nagumo exhibit realistic features observed in empirical data* complex re-entry phenomena like figure-8 and spirals* exhibits soliton-like and breather-like solutions * complex wave interactions like annihilation* Fitzhugh-Nagumo ion-currents a bit like phi-four potential: Propagation models like Fitzhugh-Nagumo exhibit realistic features observed in empirical data * complex re-entry phenomena like figure-8 and spirals * exhibits soliton-like and breather-like solutions * complex wave interactions like annihilation * Fitzhugh-Nagumo ion-currents a bit like phi-four potential Complex Patterns from Fitzhugh-Nagumo models What drives pattern formation?* sinus rhythm → Wenkebach effect* damaged substrate → re-entry* feed-back mechanism to sinus node drives atrium fibrilation: What drives pattern formation? * sinus rhythm → Wenkebach effect * damaged substrate → re-entry * feed-back mechanism to sinus node drives atrium fibrilation Complex Patterns from Fitzhugh- Nagumo class models Which parameters drive complex pattern formation, e.g. afib?* extension to sinus node uSN(t)* feed-back mechanism to sinus node drives atrium fibrilation* model parameters and critical bifurcations : Which parameters drive complex pattern formation, e.g. afib? * extension to sinus node uSN(t) * feed-back mechanism to sinus node drives atrium fibrilation * model parameters and critical bifurcations Complex Patterns from Fitzhugh- Nagumo class models Slide34: * 2D Multifractal wavelet analysis of patterns and turbulence * Wavelet formulation and analysis of shockwaves Spatiotemporal Analysis with Wavelets Slide35: 2D Fractal wavelet analysis of turbulence, A. Arneodo, 2003 Wavelet analysis of blood flow singularities by using ultrasound data, Ph. May, 2002 Multifractal wavelet analysis of 2D-rheological patterns, R.L. Westra, 2001 2D Multifractal analysis of turbulence with wavelets Slide36: Wavelet formulation of PDEs and analysis of shockwaves Cheng, H.K., Lee, C.J. and Edwards, J., 2001, Sonic Boom Noise Penetration Under a Wavy Ocean, Fatkullin I. and Hesthaven J. S., Adaptive High-Order Finite-Difference Method for Nonlinear Wave Problems, J. Scientific Computation., 2001 Vasilyev, O. V., and Paolucci, S. A dynamically adaptive multilevel wavelet collocation method for solving partial differential equations in a finite domain. J. Comput. Phys., 1996. Wavelet and multiscale techniques for the Numerical Analysis of PDEs, Kunoth, A, SIAM J. on Num. Analysis, 2002 Shockwaves Analysis with Wavelets Slide37: UM Department of cardiology/CARIM: generation of annotated spatiotemporal data interpretation of theoretic findings validation of theoretic predictions (e.g. damaged substrate) UM Department of mathematics: Analysis of empirical data Prediction of behavior Multidisciplinary approach Slide38: UM Department of cardiology/CARIM: UM Department of mathematics: Multidisciplinary approach No funding requested in this proposal Requests funding in this proposal Slide39: Consortium and links Maastricht Instruments Medtronic Bakken Research UM mathematics UM Physiology, UM Cardiology, CARIM Slide40: Relation with other internal research * Nationally STW research project BIOSENS DTC.6418 Biosens - the relation between ECG morphological analysis and the pathological condition of the heart - partner: tech. university Delft (Nl), co-funder: Medtronic (USA) - start: March 2004, end: March 2008 - proposer and local project leader: R.L. Westra * European Union research project NiSIS FP6-013569 - Mathematical modeling, analysis and identification of gene regulatory networks - direct partners: universities of Vienna (Au) and Jena (Ger) - start: February 2005, end: February 2008 - co-proposer and local project leader: R.L. Westra Slide41: Affiliation with other research groups * UM\math is member of the Dutch Institute of Systems and Control (DISC) research school of the KNAW (Royal Dutch Academy of Sciences). * Close co-operation with Technical University Delft and the Delft Institute of Microelectronics and Submicron Technology DIMES through BIOSENS. * Close co-operation with University of Aachen and Jena (GER) and Vienna (AU) through NiSIS. * Numerous contacts with other groups in and outside the Netherlands Slide42: Slide43: BIOSENS = BIOmedical Signal Processing Platform for Low-Power Real- Time SENSing of Cardiac Signals NWO/STW Slide44: BIOSENS Objective Utilizing morphological features of ECGs in novel prototypes of pacemaker and ICD front-ends. Slide45: TU Delft microelectronics Low-voltage low-power analog electronics for biomedical radio-frequency applications UM mathematics – Systems Theory Group Signal analysis, mathematical morphology , clustering and classification Research Objectives Slide46: BIOSENS support group Substantial support of : Medtronic Bakken Research Center World leader in medical implantable technology. Other biotechnology partners are: Maastricht Instruments, SystematIC Design, Twente Medical Systems International. Vitatron, Weijand Randamp;D Consultancy Slide47: Complex Pattern Formation in Electrophysiological Wave Propagation on Cardiac Walls You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.
NWO 2004 Charlie Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 155 Category: Education License: All Rights Reserved Like it (0) Dislike it (0) Added: June 18, 2007 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Slide1: Ronald L. Westra Department of Mathematics Maastricht University Complex Pattern Formation in Electrophysiological Wave Propagation on Cardiac Walls Slide2: Problem formulation Outline of proposed research Empirical observations and experimental equipment Theoretical framework and complex behavior Spatiotemporal analysis with wavelets Multidisciplinary approach Relation to other research Items in this Presentation Slide3: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Problem formulation Slide4: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Answer: yes, if the substrate is damaged Problem formulation Slide5: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Answer: yes, if the substrate is damaged Question 2: What about complex patterns if the substrate is not damaged? Problem formulation Slide6: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Answer: yes, if the substrate is damaged Question 2: What about complex patterns if the substrate is not damaged? Q3: In what language should we characterize these patterns? Problem formulation Slide7: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Answer: yes, if the substrate is damaged Question 2: What about complex patterns if the substrate is not damaged? Q3: In what language should we characterize these patterns? Q4: How to associate them with pathological conditions? Problem formulation Slide8: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Answer: yes, if the substrate is damaged Question 2: What about complex patterns if the substrate is not damaged? Q3: In what language should we characterize these patterns? Q4: How to associate them with pathological conditions? Q5: What are the driving parameters and their critical values? Problem formulation Slide9: Question: Can complex spatiotemporal patterns in electrophysiological waves be related to a pathological condition of the heart? Answer: yes, if the substrate is damaged Question 2: What about complex patterns if the substrate is not damaged? Q3: In what language should we characterize these patterns? Q4: How to associate them with pathological conditions? Q5: What are the driving parameters and their critical values? Problem formulation Q6: Can the dynamical systems approach and wavelet analysis be useful? Slide10: * Mathematical framework for modelling of electrophysiological wave propagation and analysis of the resulting patterns * Analysis of annotated empirical spatiotemporal data * Identification of abnormal substrate * Dynamic analysis of complex behavior Outline of proposed research Slide11: Spatiotemporal annotated data from in-vivo experiments Dynamical systems approach Fractal wavelet analysis Identification techniques Methodology andamp; approach Slide12: Background: 1D-pattern analysis The ECG with the characteristic P,Q,R,S,T components (van Einthoven, 1936) Slide13: 1D-Morphological analysis : Traditionally, ECG-signal analysis is focussed on the single R-wave event signaling the occurrence of cardiac depolarization, morphological attributes of the electrogram are not taken into account. Slide14: 1D-Morphological analysis : Relation between the shape of the part and possible cardiopathologies Slide15: 1D-Morphological analysis : Wavelets are superior in identifying the characteristic parts of the signal. Slide16: Direct observation of electrophysiological waves on cardiac walls, using MRI or array of electrodes Regular behavior in case of normal substrate and normal pacing Complex behavior in case of damaged substrate , fast pacing (Wenkebach), and e.g. some types of atrium fibrilation (afib III) 2D-spatiotemporal pattern analysis ‘Spoon’ of about 3 cm2 with matrix ofelectrodes with grid spacing 0.2 mm and sampling frequency of > 1 kHz: ‘Spoon’ of about 3 cm2 with matrix of electrodes with grid spacing 0.2 mm and sampling frequency of andgt; 1 kHz 2D-spatiotemporal pattern analysis Spoon is positioned on the cardiac wall: Spoon is positioned on the cardiac wall 2D-spatiotemporal pattern analysis Electrostatic potential is measured: Electrostatic potential is measured 2D-spatiotemporal pattern analysis electrostatic field fractal wave type Normal: Normal Empirical Observations Regular, quasi-periodic, soliton-like wave fronts Atrium fibrilation type III: Atrium fibrilation type III Empirical Observations ‘Figure 8’ re-entry ‘spiral-wave’ re-entry Wave annihilation * The electrostatic field propagates in soliton-like, well-localized shockfront waves* A normal heart exhibits regular quasi-periodic waves seemingly without dispersion or dissipation* Damaged substrate causes irregular behavior like re-entry phenomena * Re-entry phenomena can also be caused by refractory substrate* Other complex patterns like wave-annihilation or breathers not-related to substrate* Some parameters drive complex behavior like period of sinus rhythm and the Wenkebach-effect : * The electrostatic field propagates in soliton-like, well-localized shockfront waves * A normal heart exhibits regular quasi-periodic waves seemingly without dispersion or dissipation * Damaged substrate causes irregular behavior like re-entry phenomena * Re-entry phenomena can also be caused by refractory substrate * Other complex patterns like wave-annihilation or breathers not-related to substrate * Some parameters drive complex behavior like period of sinus rhythm and the Wenkebach-effect Empirical Observations Slide23: Propagation of electrostatic potential over cardiac substrate * substrate properties: capacity , conductivity , and parameters * diffusion-like equation causing dispersion and exponential extinction * extinction is prevented by active ion-currents exhibiting a refractory period in substrate Microscopic Equations Propagation of electrophysiological waves [1]: electrostatic potential scalar capacity density scalar 1st order approximation conductivity tensor 1st order approximation (Ohm) model parameter vectors Propagation of electrophysiological waves [1] Propagation of electrophysiological waves [2]: food supply/inhibition scalar Ion reaction currents Phenomenological models Propagation of electrophysiological waves [2] Propagation of electrophysiological waves [3]: * Ion reaction current involves many terms: * These terms are individually modeled empirically by piecewise-linear equations * Iion can also be modeled phenomenologically, as by Luo-Rudy and Fitzhugh-Nagumo, e.g. : * The food supply/inhibition scalar f can also be phenomenological entity, e.g. Fitzhugh-Nagumo: Propagation of electrophysiological waves [3] Slide27: Parametrized PDE models like Fitzhugh-Nagumo allow for the identification of damaged substrate from empirical spatiotemporal data using e.g. nonlinear least-squares methods Westra R.L., Haldermans Ph., Peeters R.L.M., 2004 Identification of damaged substrate Normal: data substrate identified from data: Identification of damaged substrate Normal: data substrate identified from data Afib III: data substrate identified from data: Identification of damaged substrate Afib III: data substrate identified from data Complex patterns need not associate with damaged substrate:Hoekstra (2000) calls atrium fibrillation a ‘dynamical disease’Aliev, Panfilov (1998): period of sinus rhythm drives the Wenkebach-effect, Sidorov, Aliev, et al. (2003), Spatiotemporal Dynamics of Damped Propagation in Excitable Cardiac Tissue, Phys. Rev. Letters 2003Gray RA, et al., Nonstationary vortexlike reentrant activity as a mechanism of polymorphic ventricular tachycardia. Circulation 1995: Complex patterns need not associate with damaged substrate: Hoekstra (2000) calls atrium fibrillation a ‘dynamical disease’ Aliev, Panfilov (1998): period of sinus rhythm drives the Wenkebach-effect, Sidorov, Aliev, et al. (2003), Spatiotemporal Dynamics of Damped Propagation in Excitable Cardiac Tissue, Phys. Rev. Letters 2003 Gray RA, et al., Nonstationary vortexlike reentrant activity as a mechanism of polymorphic ventricular tachycardia. Circulation 1995 Pattern Formation and the Emergence of Complexity Propagation models like Fitzhugh-Nagumo exhibit realistic features observed in empirical data* complex re-entry phenomena like figure-8 and spirals* exhibits soliton-like and breather-like solutions * complex wave interactions like annihilation* Fitzhugh-Nagumo ion-currents a bit like phi-four potential: Propagation models like Fitzhugh-Nagumo exhibit realistic features observed in empirical data * complex re-entry phenomena like figure-8 and spirals * exhibits soliton-like and breather-like solutions * complex wave interactions like annihilation * Fitzhugh-Nagumo ion-currents a bit like phi-four potential Complex Patterns from Fitzhugh-Nagumo models What drives pattern formation?* sinus rhythm → Wenkebach effect* damaged substrate → re-entry* feed-back mechanism to sinus node drives atrium fibrilation: What drives pattern formation? * sinus rhythm → Wenkebach effect * damaged substrate → re-entry * feed-back mechanism to sinus node drives atrium fibrilation Complex Patterns from Fitzhugh- Nagumo class models Which parameters drive complex pattern formation, e.g. afib?* extension to sinus node uSN(t)* feed-back mechanism to sinus node drives atrium fibrilation* model parameters and critical bifurcations : Which parameters drive complex pattern formation, e.g. afib? * extension to sinus node uSN(t) * feed-back mechanism to sinus node drives atrium fibrilation * model parameters and critical bifurcations Complex Patterns from Fitzhugh- Nagumo class models Slide34: * 2D Multifractal wavelet analysis of patterns and turbulence * Wavelet formulation and analysis of shockwaves Spatiotemporal Analysis with Wavelets Slide35: 2D Fractal wavelet analysis of turbulence, A. Arneodo, 2003 Wavelet analysis of blood flow singularities by using ultrasound data, Ph. May, 2002 Multifractal wavelet analysis of 2D-rheological patterns, R.L. Westra, 2001 2D Multifractal analysis of turbulence with wavelets Slide36: Wavelet formulation of PDEs and analysis of shockwaves Cheng, H.K., Lee, C.J. and Edwards, J., 2001, Sonic Boom Noise Penetration Under a Wavy Ocean, Fatkullin I. and Hesthaven J. S., Adaptive High-Order Finite-Difference Method for Nonlinear Wave Problems, J. Scientific Computation., 2001 Vasilyev, O. V., and Paolucci, S. A dynamically adaptive multilevel wavelet collocation method for solving partial differential equations in a finite domain. J. Comput. Phys., 1996. Wavelet and multiscale techniques for the Numerical Analysis of PDEs, Kunoth, A, SIAM J. on Num. Analysis, 2002 Shockwaves Analysis with Wavelets Slide37: UM Department of cardiology/CARIM: generation of annotated spatiotemporal data interpretation of theoretic findings validation of theoretic predictions (e.g. damaged substrate) UM Department of mathematics: Analysis of empirical data Prediction of behavior Multidisciplinary approach Slide38: UM Department of cardiology/CARIM: UM Department of mathematics: Multidisciplinary approach No funding requested in this proposal Requests funding in this proposal Slide39: Consortium and links Maastricht Instruments Medtronic Bakken Research UM mathematics UM Physiology, UM Cardiology, CARIM Slide40: Relation with other internal research * Nationally STW research project BIOSENS DTC.6418 Biosens - the relation between ECG morphological analysis and the pathological condition of the heart - partner: tech. university Delft (Nl), co-funder: Medtronic (USA) - start: March 2004, end: March 2008 - proposer and local project leader: R.L. Westra * European Union research project NiSIS FP6-013569 - Mathematical modeling, analysis and identification of gene regulatory networks - direct partners: universities of Vienna (Au) and Jena (Ger) - start: February 2005, end: February 2008 - co-proposer and local project leader: R.L. Westra Slide41: Affiliation with other research groups * UM\math is member of the Dutch Institute of Systems and Control (DISC) research school of the KNAW (Royal Dutch Academy of Sciences). * Close co-operation with Technical University Delft and the Delft Institute of Microelectronics and Submicron Technology DIMES through BIOSENS. * Close co-operation with University of Aachen and Jena (GER) and Vienna (AU) through NiSIS. * Numerous contacts with other groups in and outside the Netherlands Slide42: Slide43: BIOSENS = BIOmedical Signal Processing Platform for Low-Power Real- Time SENSing of Cardiac Signals NWO/STW Slide44: BIOSENS Objective Utilizing morphological features of ECGs in novel prototypes of pacemaker and ICD front-ends. Slide45: TU Delft microelectronics Low-voltage low-power analog electronics for biomedical radio-frequency applications UM mathematics – Systems Theory Group Signal analysis, mathematical morphology , clustering and classification Research Objectives Slide46: BIOSENS support group Substantial support of : Medtronic Bakken Research Center World leader in medical implantable technology. Other biotechnology partners are: Maastricht Instruments, SystematIC Design, Twente Medical Systems International. Vitatron, Weijand Randamp;D Consultancy Slide47: Complex Pattern Formation in Electrophysiological Wave Propagation on Cardiac Walls