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Predicting and controlling complex cardiac excitation waves in the heart

Christophe LETELLIER
30/06/2023
Ulrich Parlitz
The myocardium is an electrically excitable medium that supports various types of excitation waves, including stable or chaotic spiral waves that cause life-threatening arrhythmias such as ventricular fibrillation. Developing less invasive low-energy methods to terminate these arrhythmias requires a deeper understanding of the underlying chaotic spatiotemporal dynamics and novel control methods [1] [2] [3] [4]. Another challenge is the limited experimental and clinical observability of cardiac electromechanical motion due to measurement limitations that require powerful tools for data analysis and data driven modelling [5] [6]. In this talk, we will discuss these aspects of cardiac dynamics and demonstrate how concepts of nonlinear dynamics and machine learning can provide advances in the diagnosis and treatment of cardiac arrhythmias.
Voir ce site : Ulrich Parlitz

[1] T. Lilienkamp, U. Parlitz & S. Luther, Non-monotonous dose tesponse function of the termination of spiral wave chaos,Scientific Reports, 12, 12043, 2022.

[2] T. Lilienkamp, U. Parlitz & S. Luther, Taming cardiac arrhythmias : Terminating spiral wave chaos by adaptive deceleration pacing, Chaos, 32, 121105, 2022.

[3] J. Steyer, T. Lilienkamp, S. Luther & U. Parlitz, The role of pulse timing in cardiac defibrillation, Frontiers in Network Physiology, 2, 1007585, 2023.

[4] M. Aron, T. Lilienkamp, S. Luther & U. Parlitz, Optimising low-energy defibrillation in 2D cardiac tissue with a genetic algorithm, Frontiers in Network Physiology, 3, 1172454, 2023.

[5] S. Herzog, R. S. Zimmermann, J. Abele, S. Luther & U. Parlitz, Reconstructing complex cardiac excitation waves from incomplete data using echo state networks and convolutional autoencoders, Frontiers in Applied Mathematics and Statistics, 6, 616584, 2021.

[6] R. Stenger, S. Herzog, I. Kottlarz, B. R\"uchardt, S. Luther, F. Wörgötter & U. Parlitz, Reconstructing in-depth activity for chaotic 3D spatiotemporal excitable media models based on surface data, Chaos 33, 013134, 2023.


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