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Synthesising robust and optimal parameters for cardiac pacemakers using symbolic and evolutionary computation techniques.

Marta Kwiatkowska‚ Alexandru Mereacre‚ Nicola Paoletti and Andrea Patanè


We consider the problem of automatically finding safe and robust values of timing parameters of cardiac pacemaker models so that a quantitative objective, such as the pacemaker energy consumption or its cardiac output (a heamodynamic indicator of the human heart), is optimised in a finite path. The models are given as parametric networks of timed I/O automata with data, which extend timed I/O automata with priorities, real variables and real-valued functions, and specifications as Counting Metric Temporal Logic (CMTL) formulas. We formulate the parameter synthesis as a bilevel optimisation problem, where the quantitative objective (the outer problem) is optimised in the solution space obtained from optimising an inner problem that yields the maximal robustness for any parameter of the model. We develop an SMT-based method for solving the inner problem through a discrete encoding, and combine it with evolutionary algorithms and simulations to solve the outer optimisation task. We apply our approach to the composition of a (non-linear) multi-component heart model with the parametric dual chamber pacemaker model in order to find the values of multiple timing parameters of the pacemaker for different heart diseases.

Oxford‚ UK
Department of Computer Science