Adaptive filter theory
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
ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
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
Expert Systems with Applications: An International Journal
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
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In this study, carotid arterial 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. Results were classified using complex-valued artificial neural network (CVANN). Principal component analysis (PCA) and fuzzy c-means clustering (FCM) algorithm were used to make a CVANN system more effective. For this aim, before classifying with CVANN, PCA method was used for feature extraction in PCA-CVANN architecture and FCM algorithm was used for data set reduction in FCM-CVANN architecture. Training and test data were selected randomly using 10-fold cross validation. PCA-CVANN and FCM-CVANN architectures classified healthy and unhealthy subjects for training and test data with about 100% correct classification rate. These results shown that PCA-CVANN and FCM-CVANN classified Doppler signals successfully.