Modular code generation for emulating the electrical conduction system of the human heart

We study the problem of modular code generation for emulating the electrical conduction system of the heart, which is essential for the validation of implantable devices such as pacemakers. In order to develop high fidelity models, it is essential to consider the operation of hundreds, if not millions of conduction elements, called nodes of the heart. Published results so far, however, have considered a maximum of 33 nodes
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, modelled as Hybrid Input Output Automata (HIOA). The behaviour of this model is captured using the well known commercial tool Simulink
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. These approaches are limiting due to the lack of model fidelity of the conduction system. In this paper, we first develop a semantic preserving modular compilation approach for a network of HIOA, by proposing to translate them to a network of Finite State Machines (FSMs). We then demonstrate that a delayed synchronous composition of the cardiac nodes enables modular code generation that is both semantic preserving and efficient. In addition to the above example, we have developed several examples from other domains to compare Simulink
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and the developed tool called Piha. The results show that we are able to generate code which, for the cardiac model, is 60% smaller in binary size while executing 20 times faster when compared to Simulink
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.