Learning and Detecting Emergent Behavior in Networks of Cardiac Myocytes

Authors:
R. Grosu;E. Bartocci;F. Corradini;E. Entcheva;S. A. Smolka;A. Wasilewska
Affiliations:
Department of Computer Science, Stony Brook University, Stony Brook, USA NY 11794-4400;Department of Computer Science, Stony Brook University, Stony Brook, USA NY 11794-4400 and Department of Mathematics and Computer Science, University of Camerino, Camerino (MC), Italy I-62032;Department of Mathematics and Computer Science, University of Camerino, Camerino (MC), Italy I-62032;Department of Biomedical Engineering, Stony Brook University, Stony Brook, USA NY 11794-8181;Department of Computer Science, Stony Brook University, Stony Brook, USA NY 11794-4400;Department of Computer Science, Stony Brook University, Stony Brook, USA NY 11794-4400
Venue:
HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
Year:
2008

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Abstract

We address the problem of specifying and detecting emergent behavior in networks of cardiac myocytes, spiral electric waves in particular, a precursor to atrial and ventricular fibrillation. To solve this problem we: (1) Apply discrete mode-abstraction to the cycle-linear hybrid automata (clha) we have recently developed for modeling the behavior of myocyte networks; (2) Introduce the new concept of spatial-superpositionof clhamodes; (3) Develop a new spatial logic, based on spatial-superposition, for specifying emergent behavior; (4) Devise a new method for learning the formulae of this logic from the spatial patterns under investigation; and (5) Apply bounded model checking to detect (within milliseconds) the onset of spiral waves. We have implemented our methodology as the Emeraldtool-suite, a component of our ehaframework for specification, simulation, analysis and control of excitable hybrid automata. We illustrate the effectiveness of our approach by applying Emeraldto the scalar electrical fields produced by our CellExcitesimulator.