Synergies between Statistical Data Analysis and Neural Networks in the Control of Rotary Blood Pumps

In this paper we report about the application of multilayer percep-trons to three important tasks in the control of rotary blood pumps, namely the estimation of left atrium pressure, and the indication of suction, as well as danger of suction, in the left atrium. Special focus is laid on the value of traditional techniques for statistical data analysis such as principal component analysis (PCA) as tools to guide the design of the neural network solution. Eleven parameters derived from actual measurements during the performance of the pump were available as input for the three tasks. With the help of PCA, they could be reduced to three major components and thus visualized. This visualisation served as guidance for the proper choice of network, and as a tool for initialization to improve learning.