General anesthesia plays a fundamental role to provide surgeons with adequate conditions for operation and avoid discomfort or pain for the patient while reducing the negative post-operation effects of anesthesia. In medical practice, anesthesia concerns the monitoring and controlling of the evolution of the areflexia (lack of movement), analgesia (lack of pain) and hypnosis (lack of consciousness) of the patient. Based on several physiological signals, like the Bispectral Index (BIS), the electroencephalogram (EEG) and the pain and neuromuscular blockage indicators, the anesthesiologist modulates the different drugs perfusion rates to reach and maintain the adequate anesthesia levels. Besides controlling the patient’s sedation level, the anesthesiologist is designated to monitor the hemodynamic state, measured by the mean arterial pressure (MAP) and the cardiac output (CO), since the cardiovascular system strongly interacts with the multi-drug anesthesia process. The main objective of anesthesia is to maintain the desired level of hypnosis, areflexia and analgesia to facilitate the surgeon’s tasks by avoiding both drug overdosing and underdosing and their potentially extremely severe consequences on the patient. Pursuing this aim, automatic feedback control theory, formal verification and machine learning can be of great help not only to increase the control efficiency and the monitoring reliability, but also to preserve the vigilance of anesthetists on potential critical events. Several sources of complexity, though, contribute to make the problem of monitoring, predicting and controlling the anesthesia process extremely challenging. Although some works have been appearing proposing automatic control application to the anesthesia process, several key issues are worth to be further addressed.
Data-based Anesthesia Process Modelling for Online Monitoring and Prediction
Related Lab Topic :