Multimode automatized system for electroacupuncture diagnostics

Abstract

Authors proposed a novel scheme of electroacupuncture measurements for a patient state diagnostics. Diagnostication reliability increases due to sufficiently long period of statistical monitoring when acquisition of redundant data takes place. The data includes various electrical parameters in 24 special representative points. Selection of the most informative points and parameters is executed by means of jointly used elements of artificial intelligence, namely neural networks and genetic algorithm. Optimized by such a way measurements allow to get a fast diagnostic result with help of a learned neural network.