Implementing wavelet/probabilistic neural networks for Doppler ultrasound blood flow signals

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.