Noninvasive detection of mechanical prosthetic heart valve disorder

Zhang, Di; He, Jiazhong; Yao, Jianping; Wu, Yuequan; Du, Min

doi:10.1016/j.compbiomed.2012.06.002

Cited by 10 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Motivated by the ideas in [11,13], a new feature extraction algorithm was proposed in this paper. The proposed method uses both locality preserving kernel Fisher discriminant analysis and local discriminant bases (LDB), to integrate both global and local information of training samples, and achieve higher classification rates as well.…”

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%

“…As in [11,12], total 5 different kinds of mechanical prosthetic heart valves sounds are studied in this paper, including valve stenosis & perivalvular leakage (VSPL), normal (NOR), valve obstruction (VO), perivalvular leakage (PL), and valve stenosis (VS). We totally collect 61 abnormal sounds and 210 normal sounds (18 people have PL, 10 people have VO, 21 people have VS, 12 people have VSPL).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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)

Self Cite

View full text Add to dashboard Cite

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%.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The applications of data mining methods for the development of computer systems analyzing data related to cardiac patients are very important and have been investigated, for example, in the recent articles [1][2][3][4][5][6]. Cardiac autonomic neuropathy (CAN) is a diabetes complication due to abnormal functioning of the autonomic nervous system, which may be associated with sudden cardiac death [7,8].…”

Section: Introductionmentioning

confidence: 99%

Predicting cardiac autonomic neuropathy category for diabetic data with missing values

Abawajy

Kelarev

Chowdhury

et al. 2013

Computers in Biology and Medicine

View full text Add to dashboard Cite

Cardiovascular autonomic neuropathy (CAN) is a serious and well known complication of diabetes. Previous articles circumvented the problem of missing values in CAN data by deleting all records and fields with missing values and applying classifiers trained on different sets of features that were complete. Most of them also added alternative features to compensate for the deleted ones. Here we introduce and investigate a new method for classifying CAN data with missing values. In contrast to all previous papers, our new method does not delete attributes with missing values, does not use classifiers, and does not add features. Instead it is based on regression and meta-regression combined with the Ewing formula for identifying the classes of CAN. This is the first article using the Ewing formula and regression to classify CAN. We carried out extensive experiments to determine the best combination of regression and meta-regression techniques for classifying CAN data with missing values. The best outcomes have been obtained by the additive regression meta-learner based on M5Rules and combined with the Ewing formula. It has achieved the best accuracy of 99.78% for two classes of CAN, and 98.98% for three classes of CAN. These outcomes are substantially better than previous results obtained in the literature by deleting all missing attributes and applying traditional classifiers to different sets of features without regression. Another advantage of our method is that it does not require practitioners to perform more tests collecting additional alternative features.

show abstract

RenyiBS: Renyi entropy basis selection from wavelet packet decomposition tree for phonocardiogram classification

Safara

Ramaiah

2020

J Supercomput

View full text Add to dashboard Cite

Noninvasive detection of mechanical prosthetic heart valve disorder

Cited by 10 publications

References 20 publications

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

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

Predicting cardiac autonomic neuropathy category for diabetic data with missing values

RenyiBS: Renyi entropy basis selection from wavelet packet decomposition tree for phonocardiogram classification

Contact Info

Product

Resources

About