Fuzzy logic controller for hemodialysis machine based on human body model

Abstract

Fuzzy controllers are being used in various control schemes. The aim of this study is to adjust the hemodialysis machine parameters by utilizing a fuzzy logic controller (FLC) so that patient's hemodynamic condition remains stable during hemodialysis treatment. For this purpose, a comprehensive mathematical model of the arterial pressure response during hemodialysis, including hemodynamic, osmotic, and regulatory phenomena has been used. The multi-input multi-output (MIMO) fuzzy logic controller receives three parameters from the model (heart rate, arterial blood pressure, and relative blood volume) as input. According to the changes in the controller input values and its rule base, the outputs change so that the patient's hemodynamic condition remains stable. The results of the simulations illustrate that applying the controller can improve the stability of a patient's hemodynamic condition during hemodialysis treatment and it also decreases the treatment time. Furthermore, by using fuzzy logic, there is no need to have prior knowledge about the system under control and the FLC is compatible with different patients.

Optimized Blood Volume Monitoring during Hemodialysis Procedure based on Ultrasonic Speed Measurement

“…As it was mentioned, blood volume monitor (BVM) can be used as a controlling system for estimating each treatment symptom or each patient’s activity (such as drinking during dialysis), which can affect the patient’s blood water volume. In addition, a simple feedback can be obtained for the control system of the ultrafiltration rate from the blood water volume by BVM [6,7]. A separate block has been designed for this measurement in some hemodialysis machines [9].…”

“…It is concluded that controlling the blood water volume is specifically important in the dynamic stability of the cardiovascular system [6-8]. To gain desired blood water volume, the relative blood water volume (RBV) is defined.…”

Development of Fuzzy Based Autoregressive Moving Average Exogenous Input Model for Water Flow in Nigerian Kanji Hydro-Power Dam

“…was accounted for by using Takagi-Sugeno (TS) fuzzy system approximated as Multi Input Single Output (MISO) TS fuzzy system according to: Gaussian membership function was adopted to reduce the effect of noise in the signal. It has low sensitivity to small changes in input parameters than the singleton membership function (Nafisi, Eghbal, Motlagh and Yavari, 2011). The two parameters that characterized Gaussian membership functions as shown in Equation 6are the center, 𝑐 and the width, 𝜎…”