Detection of heart valve diseases by using fuzzy discrete hidden Markov model

Authors:
Harun Uğuz;Ahmet Arslan;Rıdvan Saraçoğlu;İbrahim Türkoğlu
Affiliations:
Department of Computer Engineering, Selçuk University, Konya, Turkey;Department of Computer Engineering, Selçuk University, Konya, Turkey;Department of Electronic and Computer Education, Selçuk University, Konya, Turkey;Department of Electronics and Computer Education, Firat University, Elazığ, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

Citing 5
Cited 2

Classification by fuzzy integral: performance and tests

Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases

Pattern Recognition Letters
Generalized hidden Markov models. I. Theoretical frameworks

IEEE Transactions on Fuzzy Systems
Generalized hidden Markov models. II. Application to handwritten word recognition

IEEE Transactions on Fuzzy Systems
Entropy-based algorithms for best basis selection

IEEE Transactions on Information Theory - Part 2

Classification of internal carotid artery Doppler signals using fuzzy discrete hidden Markov model

Expert Systems with Applications: An International Journal
Biomedical system based on the Discrete Hidden Markov Model using the Rocchio-Genetic approach for the classification of internal carotid artery Doppler signals

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	12.05

Visualization

Abstract

In the present study, biomedical based application was developed to classify the data belongs to normal and abnormal samples generated by Doppler ultrasound. This study consists of raw data obtaining and pre-processing, feature extraction and classification steps. In the pre-processing step, a high-pass filter, white de-noising and normalization were used. During the feature extraction step, wavelet entropy was applied by wavelet transform and short time fourier transform. Obtained features were classified by fuzzy discrete hidden Markov model (FDHMM). For this purpose, a FDHMM that consists of Sugeno and Choquet integrals and @l fuzzy measurement was defined to eliminate statistical dependence assumptions to increase the performance and to have better flexibility. Moreover, Sugeno integral was used together with triangular norms that are mentioned frequently in the literature in order to increase the performance. Experimental results show that recognition rate obtained by Sugeno fuzzy integral with triangular norm is more successful than recognition rates obtained by standard discrete HMM (DHMM) and Choquet integral based FDHMM. In addition to this, it is shown in this study that the performance of the Sugeno integral based method is better than the performances of artificial neural network (ANN) and HMM based classification systems that were used in previous studies of the authors.