Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
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
Complex-valued wavelet network
Journal of Computer and System Sciences
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Evaluation Methods in Biomedical Informatics (Health Informatics)
Evaluation Methods in Biomedical Informatics (Health Informatics)
Computers in Biology and Medicine
A fuzzy clustering neural network architecture for classification of ECG arrhythmias
Computers in Biology and Medicine
Artificial Intelligence in Medicine
A low-cost screening method for the detection of the carotid artery diseases
Knowledge-Based Systems
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Objective: In this paper, the new complex-valued wavelet artificial neural network (CVWANN) was proposed for classifying Doppler signals recorded from patients and healthy volunteers. CVWANN was implemented on four different structures (CVWANN-1, -2, -3 and -4). Materials and methods: 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. In implemented structures in this paper, Haar wavelet and Mexican hat wavelet functions were used as real and imaginary parts of activation function on different sequence in hidden layer nodes. CVWANN-1, -2 -3 and -4 were implemented by using Haar-Haar, Mexican hat-Mexican hat, Haar-Mexican hat, Mexican hat-Haar as real-imaginary parts of activation function in hidden layer nodes, respectively. Results and conclusion: In contrast to CVWANN-2, which reached classification rates of 24.5%, CVWANN-1, -3 and -4 classified 40 healthy and 38 unhealthy subjects for both training and test phases with 100% correct classification rate using leave-one-out cross-validation. These networks have 100% sensitivity, 100% specifity and average detection rate is calculated as 100%. In addition, positive predictive value and negative predictive value were obtained as 100% for these networks. These results shown that CVWANN-1, -3 and -4 succeeded to classify Doppler signals. Moreover, training time and processing complexity were decreased considerable amount by using CVWANN-3. As conclusion, using of Mexican hat wavelet function in real and imaginary parts of hidden layer activation function (CVWANN-2) is not suitable for classifying healthy and unhealthy subjects with high accuracy rate. The cause of unsuitability (obtaining the poor results in CVWANN-2) is lack of harmony between type of activation function in hidden layer and type of input signals in neural network.