Uguz, HarunArslan, AhmetTurkoglu, Ibrahim2020-03-262020-03-2620070167-86551872-7344https://dx.doi.org/10.1016/j.patrec.2006.08.009https://hdl.handle.net/20.500.12395/21161In this study, a biomedical diagnosis system for pattern recognition with normal and abnormal classes has been developed. First, feature extraction processing was made by using the Doppler Ultrasound. During feature extraction stage, Wavelet transforms and shorttime Fourier transform were used. As next step, wavelet entropy were applied to these features. In the classification stage, hidden Markov model (HMM) was used. To compute the correct classification rate of proposed HMM classifier, it was compared to ANN by using a data set containing 215 samples. In our experiments, specificity rate and sensitivity rates of proposed HMM classifier system with fuzzy C means (FCM)/K-means algorithms were found as 92% and 97.26% respectively. The present study shows that proper selection of the HMMs initial parameter values according to FCM/K-means algorithms improves the recognition rate of the proposed system which was also compared to our previous study named ANN. (c) 2006 Elsevier B.V. All rights reserved.en10.1016/j.patrec.2006.08.009info:eu-repo/semantics/closedAccesspattern recognitionDoppler heart soundswavelet decompositionhidden Markov modelArticle284395404Q1WOS:000243625400001Q3