Performance analysis of classical, model-based and eigenvector methods: Ophthalmic arterial disorders detection case

In this study, ophthalmic arterial Doppler signals recorded from 214 subjects were processed using classical, model-based, and eigenvector methods. The classical method (fast Fourier transform), two model-based methods (Burg autoregressive, least squares modified Yule–Walker autoregressive moving average methods), and three eigenvector methods (Pisarenko, multiple signal classification, and Minimum-Norm methods) were selected for performing spectral analysis of the ophthalmic arterial Doppler signals. Doppler power spectral density estimates of the ophthalmic arterial Doppler signals were obtained using these spectrum analysis techniques. The variations in the shape of the Doppler power spectra were examined in order to detect variabilities such as stenosis, ocular Behcet disease, and uveitis disease in the physical state of ophthalmic arterial Doppler signals. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in detecting stenosis, Behcet disease and uveitis disease in ophthalmic arteries.