Anesthesia patient monitoring and control in wireless-based systems

This paper investigates impact of noise and signal averaging on patient control in anesthesia applications in wireless connected systems. Such systems involve communication channels which introduce noises due to quantization, channel noises, and have limited communication bandwidth resources. Usually signal averaging can be used effectively in reducing noise effects when remote monitoring and diagnosis are involved. However, when feedback is intended, we show that signal averaging will lose its utility substantially. To explain this phenomenon, we analyze stability margins under signal averaging and derive some optimal strategies for selecting windows size. A typical case of anesthesia depth control problems is used in this development.