Detection of atherosclerosis using autoregressive modelling and principles component analysis to carotid artery Doppler signals.

The purpose of this study was to evaluate principal component analysis method to power spectral density acquired with autoregressive modeling (AR) of carotid artery Doppler signals. Carotid artery Doppler signals from patient with atherosclerosis and healthy subjects were recorded. Afterwards, power spectral densities of these signals were obtained using AR method. The basic differences between the healthy and patients were obtained with 1st principal component obviously. These results could be extrapolated to situations involving noninvasive measurement where PCA can be extremely time saving. As a result the patient and healthy groups are separated clearly from each other via an arbitrary power function y=ax with perfect accuracy resulting in a precision sensitivity and specificity of 100 percent and the use of PCA of physiological waveform is presented as a powerful method likely to be incorporated in future medical signal processing.