2011
DOI: 10.1016/j.ijcard.2011.06.033
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Analysis and classification of heart sounds with mechanical prosthetic heart valves based on Hilbert-Huang transform

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Cited by 11 publications
(14 citation statements)
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“…to address these shortcomings in a cost-effective diagnostic tests in ambulatory monitoring, several techniques [2][3][4][5] have been proposed for automatic analysis of heart sounds.…”
Section: Introductionmentioning
confidence: 99%
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“…to address these shortcomings in a cost-effective diagnostic tests in ambulatory monitoring, several techniques [2][3][4][5] have been proposed for automatic analysis of heart sounds.…”
Section: Introductionmentioning
confidence: 99%
“…Several studies have been conducted to PCG segmentation using different envelope extraction methods such as Shannon energy [2], Shannon entropy [3], HilbertHuang transform [4], and autocorrelation [5]. The envelope of signal attenuates the noise and amplifies the low-intensity components of the signal.…”
Section: Introductionmentioning
confidence: 99%
“…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. However, their methods [11,12] were developed based on the condition that samples lie in a linear separable space, which is usually not true in most actual applications.…”
Section: Introductionmentioning
confidence: 99%
“…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. However, their methods [11,12] were developed based on the condition that samples lie in a linear separable space, which is usually not true in most actual applications. Kernel Fisher discriminant analysis (KDA) is a widely used nonlinear feature extraction technique because of its ability to extract the most discriminatory nonlinear features.…”
Section: Introductionmentioning
confidence: 99%
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