2019/09/28 by Shahin Tasoujian, Saeed Salavati, Tasoujian, Shahin +5
Engineering · Medicine · #Control Systems and Identification #FOS: Electrical engineering #Fault Detection and Control Systems #Hemodynamic Monitoring and Therapy #Signal Processing (eess.SP) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1910.01022
openalex publication_date 2019/09/28 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
Mathematical modeling and real-time dynamics identification of the mean\narterial blood pressure (MAP) response of a patient to vasoactive drug infusion\ncan provide a reliable tool for automated drug administration and therefore,\nreduce the emergency costs and significantly benefit the patient's MAP\nregulation in an intensive care unit. To this end, a dynamic first-order linear\nparameter-varying (LPV) model with varying parameters and varying input delay\nis considered to capture the MAP response dynamics. Such a model effectively\naddresses the complexity and the intra- and inter-patient variability of the\nphysiological response. We discretize the model and augment the state vector\nwith model parameters as unknown states of the system and a Bayesian-based\nmultiple-model square root cubature Kalman filtering (MMSRCKF) approach is\nutilized to estimate the model time-varying parameters. Since, unlike the other\nmodel parameters, the input delay cannot be captured by a random-walk process,\na multiple-model module with a posterior probability estimation is implemented\nto provide the delay identification. Validation results confirm the\neffectiveness of the proposed identification algorithm both in simulation\nscenarios and also using animal experiment data.\n