Adaptive filter theory
Discrete-time signal processing
Discrete-time signal processing
An Analysis of the Fundamental Structure of Complex-Valued Neurons
Neural Processing Letters
Advanced Topics in Digital Signal Processing
Advanced Topics in Digital Signal Processing
Classification of Transcranial Doppler Signals Using Artificial Neural Network
Journal of Medical Systems
Computers in Biology and Medicine
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
Complex-valued wavelet artificial neural network for Doppler signals classifying
Artificial Intelligence in Medicine
Computer Methods and Programs in Biomedicine
A New Method for Diagnosis of Cirrhosis Disease: Complex-valued Artificial Neural Network
Journal of Medical Systems
Artificial Intelligence in Medicine
Classification of internal carotid artery Doppler signals using fuzzy discrete hidden Markov model
Expert Systems with Applications: An International Journal
Fuzzy clustering complex-valued neural network to diagnose cirrhosis disease
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network
Journal of Medical Systems
A low-cost screening method for the detection of the carotid artery diseases
Knowledge-Based Systems
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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.