We present a novel approach to describe dependability measures for intelligent patient monitoring devices. The strategy is based on using a combination of methods from system theory and real-time physiological simulations. For the first time not only the technical device but also the patient is taken into consideration. Including the patient requires prediction of physiology which is achieved by a real-time physiological simulation in a continuous time domain, whereby one of the main ingredients is a temporal reasoning element. The quality of the reasoning is expressed by a dependability analysis strategy. Thereby, anomalies are expressed as differences between simulation and real world data. Deviations are detected for current and they are forecasted for future points in time and can express critical situations. By this method, patient specific differences in terms of physiological reactions are described, allowing early detection of critical states.
The early fault analysis is mandatory for safety critical systems, which are required to operate safely even on the presence of faults. System design methodologies tackle the early design and verification of systems by allowing several abstraction for their models, but still offer only digital bit faults as fault models. Therefore we develop a signal fault model for the Transaction-Level Modeling. We extend the TLM generic payload by the signal characteristics: Voltage level, delay, slope time and glitches. In order to analyze and process these, a TLM bus model is created, with which signal faults can be detected and translated to data failures. Furthermore, inserting this bus in an acquisition system and implementing fallback modes for the bus operation, the propagation of the signal faults through the system can be assessed. Simulating this model using probability distributions for the different signal faults, 5516 faults have been generated. From these, 5143 have been recovered, 239 isolated and 134 turned into failures.