Implementation of artificial neural networks and fuzzy logic for hypovolemia monitoring in the operation theatre.

Abstract

The objective of this research is to improve the patient safety during surgical procedures by providing a system for detection and classification of hypovolemia event on the basis of a vital parameters. This system analyzes three physiological data which related to blood volume in the body and regarding all this data it’ll give output .it’ll empower doctors to take their decision quickly. In this research used two systems to satisfy objective and evaluate between them, the first system is artificial neural network which trained by back propagation learning algorithm to diagnosis hypovolemia critical event, it simulated system by Graphical user interface tool(GUI).the proposed system obtained the physiological data from patient monitoring devices as testing data to artificial neural network and then give two output which indicate to the hypovolemia stage and the compensation volume of blood ,water or plasma. The second system has been designed using fuzzy logic controller which consist rule–base mechanism of fuzzy system and the controlled process to diagnosis hypovolemia critical event ,these system is simulated by using simulation program .the proposed system obtained the physiological data from patient monitoring devices as inputs to fuzzy control system which decide depend on rule-base and the degree of member ship function of each input value to fuzzy set or linguistic term and then give two output which indicate to the hypovolemia stage and the compensation volume of blood ,water or plasma. The simulation results show an acceptable performance or those two systems compared to the doctor decision in the hospital.