Analysis of spectral features of mechanical heart valve sounds after aortic and mitral valve replacement

Altunkaya, Sabri; Kara, Sadık; Görmüş, Niyazi; Herdem, Saadetdin

doi:10.1109/biyomut.2010.5479872

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…For mechanical bileaflet valves, Hiroshi Sugiki et al [9] utilized CWT to analyze the features of its acoustic signal. For patients with mitral and aortic valve replaced by mechanical artificial valves, Altunkaya et al [10] made a detail spectral feature comparison between S1 and S2 components. More recently, in order to classify different mechanical prosthetic heart valve sounds, Di Zhang et al [11,12] proposed a new LDB-based feature extraction method in [11] and a HHT-based feature extraction method in [12], respectively.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of prosthetic heart valve using locality preserving kernel fisher discriminant analysis and local discriminant bases

Zhang

2015

2015 8th International Conference on Biomedical Engineering and Informatics (BMEI)

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Auscultation, a method to detect the condition of heart by examining the heart sounds, is widely used by cardiologists. Using artificial intelligence methods in auscultation to detect various heart diseases is increasing in present days. In this paper, we try to classify 5 different categories of mechanical artificial heart valve sounds. Considering that such classification task is highly nonlinear, a new feature extraction algorithm, which is based on locality preserving kernel Fisher discriminant analysis and local discriminant bases (LDB), is proposed to improve the classification accuracy. All the tests are carried on a dataset that consists of 271 heart sounds. When the features extracted by the proposed method are fed into a normal linear discriminant function based (LDF) classifier, the correct classification rates can reach up to 95.6%.

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