A model-based fuzzy logic controller with Kalman filtering for tracking mean arterial pressure

This paper proposes a new noninvasive measurement method for tracking the tendency of mean arterial pressure (MAP) in the radial artery. The designed system consists of a tonometer, a microsyringe device, and a model-based fuzzy logic controller. The modified flexible diaphragm tonometer is to detect the continuous blood pressure waveform and vessel volume pulse. A precise mathematical model describing the interaction between the tonometer and artery is derived. To reach accurate measurement without distortion, a model-based fuzzy logic control system is designed to compensate the change of MAP by applying a counter pressure on the tonometer chamber through the microsyringe device. The proposed control system consists of a linear predictor, a Kalman filter, and a synthetic fuzzy logic controller (SFLC). The linear predictor is to estimate the MAPs changing tendency based on the identified arterial pressure–volume model and then to beat-to-beat adjust the function of SFLC. The Kalman filter is to reduce the physiologic and measurement disturbance of the vessel volume oscillation amplitude (VOA). The SFLC is composed of three parallel subcontrollers, each of which is a simple fuzzy logic controller, for processing the three changing states of the MAP: ascending, descending, and stabilizing states, respectively. The design of the fuzzy rules in each subcontroller is based on the oscillometric principle saying that the arterial vessel has the maximum compliance when the detected vessel volume pulse reaches its maximum amplitude. Simulation results show that, for the real physiologic MAP with changing rates up to 20 or 20 mm-Hg/minute, the model-based SFLC can beat-to-beat adjust the tonometer’s chamber pressure to follow the tendency of MAP accurately.