Premium
Feature extraction by autoregressive spectral analysis using maximum likelihood estimation: internal carotid arterial Doppler signals
Author(s) -
Übeyli Elif Derya
Publication year - 2008
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2008.00448.x
Subject(s) - autoregressive model , doppler effect , computer science , pattern recognition (psychology) , spectral density , fourier transform , spectral density estimation , artificial intelligence , statistics , mathematics , physics , telecommunications , mathematical analysis , astronomy
In this study, Doppler signals recorded from the internal carotid artery (ICA) of 97 subjects were processed by personal computer using classical and model‐based methods. Fast Fourier transform (classical method) and autoregressive (model‐based method) methods were selected for processing the ICA Doppler signals. The parameters in the autoregressive method were found by using maximum likelihood estimation. The Doppler power spectra of the ICA Doppler signals were obtained by using these spectral analysis techniques. The variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These Doppler spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in the ICA. Reliable information on haemodynamic alterations in the ICA can be obtained by evaluation of these sonograms.