Automatic Classifiers for Medical Data from Doppler Unit

Abstract

Nowadays, hand-held ultrasonic Doppler units are often used for noninvasive screening of atherosclerosis in arteries of the lower limbs. The mean velocity of blood flow in time and blood pressures are measured on several positions on each lower limb. This project presents software that is able to analyze such data and classify it in real time into selected diagnostic classes. It is also capable of giving a notice of some errors encountered during measuring. At the Department of Functional Diagnostics in the Regional Hospital of Liberec a database of several hundreds signals was collected. In cooperation with the specialist, the signals were manually classified into four classes. Consequently selected signal features were extracted and used for training a distance and a Bayesian classifier. Another set of signals was used for evaluating and optimizing the parameters of the classifiers. This paper compares the results of the software with those provided by a human expert. They agreed in 89 % cases.