Adaptive neuro-fuzzy inference system for diagnosis of the heart valve diseases using wavelet transform with entropy
Küçük Resim Yok
Tarih
2012
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SPRINGER LONDON LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Listening via stethoscope is a preferential method, being used by physicians for distinguishing normal and abnormal cardiac systems. On the other hand, listening with stethoscope has a number of constraints. The interpretation of various heart sounds depends on physician's ability of hearing, experience, and skill. Such limitations may be reduced by developing biomedical-based decision support systems. In this study, a biomedical-based decision support system was developed for the classification of heart sound signals, obtained from 120 subjects with normal, pulmonary, and mitral stenosis heart valve diseases via stethoscope. Developed system comprises of three stages. In the first stage, for feature extraction, obtained heart sound signals were separated to its sub-bands using discrete wavelet transform (DWT). In the second stage, entropy of each sub-band was calculated using Shannon entropy algorithm to reduce the dimensionality of the feature vectors via DWT. In the third stage, the reduced features of three types of heart sound signals were used as input patterns of the adaptive neuro-fuzzy inference system (ANFIS) classifiers. Developed method reached 98.33% classification accuracy, and it was showed that purposed method is effective for detection of heart valve diseases.
Açıklama
Anahtar Kelimeler
Heart sound, Discrete wavelet transform, Adaptive neuro-fuzzy inference system, Entropy
Kaynak
NEURAL COMPUTING & APPLICATIONS
WoS Q Değeri
Q3
Scopus Q Değeri
Q1
Cilt
21
Sayı
7
