Recognition of early phase of atherosclerosis using principles component analysis and artificial neural networks from carotid artery Doppler signals

Atherosclerosis means thickening and hardening of the arteries, which has dramatic effects on blood pressure, resistance and blood flow. Since angiography is invasive and has a relatively high cost, non-invasive ultrasonic Doppler sonography is generally recommended to diagnose of athersosclerosis. In this study, we have employed the sonograms depicted from Autoregressive (AR) modeling, Principles component analysis (PCA) for data reduction of Doppler sonograms and artificial neural networks (ANN) in order to distinguish between atherosclerosis and healthy subjects. The fuzzy appearance of the carotid artery Doppler signals makes physicians suspicious about the existence of diseases and causes false diagnosis. Our technique gets around this problem using ANN to decide and assist the physician to make the final judgment in confidence. The stated results show that training time and processing complexity were reduced using PCA-ANN architecture however the proposed method can make an effective interpretation and ANN classified Doppler signals successfully.