Detection of heart valve diseases by using fuzzy discrete hidden Markov model
Küçük Resim Yok
Tarih
2008
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
PERGAMON-ELSEVIER SCIENCE LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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 lambda 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. (c) 2007 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
pattern recognition, Doppler heart sounds, wavelet decomposition, fuzzy discrete hidden Markov model, triangular norms
Kaynak
EXPERT SYSTEMS WITH APPLICATIONS
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
34
Sayı
4
