This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local ar...This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local area networks, or tele-medicine over a wide area network. 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 window sizes. A typical case of anesthe-sia depth control problems is used in this development.展开更多
Conjunctive use of anesthetic agents results in drug interactions which can alter or influence multiple patient outcomes such as anesthesia depth,and cardiorespiratory parameters which can also be altered by patient c...Conjunctive use of anesthetic agents results in drug interactions which can alter or influence multiple patient outcomes such as anesthesia depth,and cardiorespiratory parameters which can also be altered by patient conditions and surgical procedures.Using artificial intelligence technology to continuously gather data of drug infusion and patient outcomes,we can generate reliable computer models individualized for a patient during specific stages of particular surgical procedures.This data can then be used to extend the current anesthesia monitoring functions to include future impact prediction,drug administration planning,and anesthesia decisions.展开更多
文摘This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local area networks, or tele-medicine over a wide area network. 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 window sizes. A typical case of anesthe-sia depth control problems is used in this development.
文摘Conjunctive use of anesthetic agents results in drug interactions which can alter or influence multiple patient outcomes such as anesthesia depth,and cardiorespiratory parameters which can also be altered by patient conditions and surgical procedures.Using artificial intelligence technology to continuously gather data of drug infusion and patient outcomes,we can generate reliable computer models individualized for a patient during specific stages of particular surgical procedures.This data can then be used to extend the current anesthesia monitoring functions to include future impact prediction,drug administration planning,and anesthesia decisions.
