Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Image Processing with Complex Daubechies Wavelets
Journal of Mathematical Imaging and Vision
Using Feature Construction to Improve the Performance of Neural Networks
Management Science
An Analysis of the Fundamental Structure of Complex-Valued Neurons
Neural Processing Letters
Digital Signal Processing: A Practical Approach
Digital Signal Processing: A Practical Approach
Classification of Transcranial Doppler Signals Using Artificial Neural Network
Journal of Medical Systems
Directional, shift-insensitive, complex wavelet transforms with controllable redundancy
Directional, shift-insensitive, complex wavelet transforms with controllable redundancy
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Complex-valued wavelet artificial neural network for Doppler signals classifying
Artificial Intelligence in Medicine
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
Hypercomplex signals-a novel extension of the analytic signal tothe multidimensional case
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
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Objective: In biomedical signal classification, due to the huge amount of data, to compress the biomedical waveform data is vital. This paper presents two different structures formed using feature extraction algorithms to decrease size of feature set in training and test data. Materials and methods: The proposed structures, named as wavelet transform-complex-valued artificial neural network (WT-CVANN) and complex wavelet transform-complex-valued artificial neural network (CWT-CVANN), use real and complex discrete wavelet transform for feature extraction. The aim of using wavelet transform is to compress data and to reduce training time of network without decreasing accuracy rate. In this study, the presented structures were applied to the problem of classification in carotid arterial Doppler ultrasound signals. Carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group included 22 males and 16 females with an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal (lower extremity) angiographies (mean age, 59 years; range, 48-72 years). Healthy volunteers were young non-smokers who seem to not bear any risk of atherosclerosis, including 28 males and 12 females (mean age, 23 years; range, 19-27 years). Results and conclusion: Sensitivity, specificity and average detection rate were calculated for comparison, after training and test phases of all structures finished. These parameters have demonstrated that training times of CVANN and real-valued artificial neural network (RVANN) were reduced using feature extraction algorithms without decreasing accuracy rate in accordance to our aim.