Title
Effects of window types on classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network
Abstract
In this study, carotid artery Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group had an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal angiographies. Doppler signals were processed using fast Fourier transform (FFT) with different window types, Hilbert transform and Welch methods. After these processes, Doppler signals were classified using complex-valued artificial neural network (CVANN). Effects of window types in classification were interpreted. Results for three methods and five window types (Bartlett, Blackman, Boxcar, Hamming, Hanning) were presented as comparatively. CVANN is a new technique for solving classification problems in Doppler signals. Furthermore, examining the effects of window types in addition to CVANN in this classification problem is also the first study in literature related with this subject. Results showed that CVANN, whose input data were processed by Welch method for each window types stated above, had classified all training and test patterns, which consist of 36 healthy, 34 unhealthy and four healthy, four unhealthy subjects, respectively, with 100% classification accuracy for both training and test phases.
Year
DOI
Venue
2007
10.1016/j.compbiomed.2006.01.008
Comp. in Bio. and Med.
Keywords
Field
DocType
Atherosclerosis,Carotid artery,Doppler signals,Complex valued ANN,Window types,FFT,Hilbert transform,Welch transform
Biomedical engineering,Computer vision,Computer science,Carotid arteries,Established diagnosis,Fast Fourier transform,Doppler ultrasound,Artificial intelligence,Doppler effect,Artificial neural network
Journal
Volume
Issue
ISSN
37
3
0010-4825
Citations 
PageRank 
References 
15
0.72
10
Authors
2
Name
Order
Citations
PageRank
Yüksel Ozbay131217.03
Murat Ceylan2808.37