Quality-Driven Continuous Adaptiation of ECG Interpretation in a Distributed Surveillance System

Principal rules defining the adaptation of ECG interpretation software in a distributed surveillance network are presented in this paper. Thanks to the pervasive access to wireless digital communication services, the intelligent monitoring networks automatically solve difficult medical cases thanks to the auto-adaptation of data interpretation and transmission to the variable patient status and technical constrains. The foundation of this innovative approach is the use of selected diagnostic parameters in a loopback modifying the running interpretive software. The auto adaptive process maximizes the general estimate of patient description quality aggregating the divergence values of particular parameters modulated by the medical relevance factor dependent on the status of patient. Our approach is motivated by the outcomes from the research on human experts behavior, statistics of the procedures reliability and usage as well as tests in a prototype client-server application. The tests yielded very promising results: the convergence of the remotely computed diagnostic outcome was achieved in over 80% of software adaptation attempts. Comparing to the rigid reporting mode, avoiding unnecessary computation extends the autonomy time by 65% and the transmission channel occupation was reduced by 3,1 to 5,6 times.