Usage of eigenvector methods to improve reliable classifier for Doppler ultrasound signals.

A new approach based on the implementation of the automated diagnostic systems for Doppler ultrasound signals classification with the features extracted by eigenvector methods is presented. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Because of the importance of making the right decision, the present work is carried out for searching better classification procedures for the Doppler ultrasound signals. Decision making was performed in two stages: feature extraction by the eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the Doppler ultrasound signals by the combination of eigenvector methods and the classifiers. The present research demonstrated that the power levels of the power spectral density (PSD) estimates obtained by the eigenvector methods are the features which well represent the Doppler ultrasound signals and the probabilistic neural networks (PNNs), recurrent neural networks (RNNs) trained on these features achieved high classification accuracies.