Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Computers in Biology and Medicine
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Learning vector quantization for the probabilistic neural network
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Journal of Medical Systems
Classification of internal carotid artery Doppler signals using fuzzy discrete hidden Markov model
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
Hi-index | 12.05 |
In this paper, we present the probabilistic neural networks (PNNs) for the Doppler ultrasound blood flow signals. The ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals were decomposed into time-frequency representations using discrete wavelet transform (DWT) and statistical features were calculated to depict their distribution. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. Our research demonstrated that the wavelet coefficients are the features which well represent the Doppler signals and the PNNs trained on these features achieved high classification accuracies.