Model-Based Monitoring and Adaptation of Pacemaker Behavior Using Hierarchical Fuzzy Colored Petri-Nets

Abstract

A pacemaker is an embedded device that is sited in the chest to regulate irregular heartbeats known as arrhythmias. Since such devices are directed by software, a software failure may cause a serious hazard such as patient death. Runtime monitoring and adaptation of the device software behavior offers a solution for preventing device hazards. We have already obtained some experiences from monitoring medical devices like: 1) insulin pump using fuzzy Petri net and 2) cardiac pacemaker using colored Petri-net (CPN) and hierarchical fuzzy CPN (HFCPN). However, these studies did not present an adaptation method for software runtime faults. This paper, extending our previous work, presents an automatic runtime method for continuous verification of the behavior of the implanted pacemaker using a software agent. The autonomy and the intelligence characteristics of the software agent are used to control the behavior of the cardiac pacemaker software by drawing inferences from a knowledge base where HFCPN is used by the agent as the inference engine. Compared to a flat inference engine, the HFCPN is able to cover the concurrent states initiated by input fuzzy values and improve the running time for finding a suitable rule by up to 92%. In addition, the intelligent software agent checks the runtime operation accuracy of the pacemaker software in vital and unexpected situations, and redirects the software decision if it finds an unacceptable value. To demonstrate the HFCPN’s behavior and decision-making in different situations, three different scenarios are presented.