Key Laboratory of Biomedical Engineering in Guangdong

Guangzhou, China

Key Laboratory of Biomedical Engineering in Guangdong

Guangzhou, China
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Deng M.,South China University of Technology | Deng M.,Key Laboratory of Biomedical Engineering in Guangdong | Tang M.,Peking Union Medical College | Wang C.,South China University of Technology | And 7 more authors.
American Journal of Cardiology | Year: 2016

Cardiodynamicsgram (CDG) has emerged recently as a noninvasive spatiotemporal electrocardiographic method for subtle cardiac dynamics information analysis within electrocardiogram (ECG). This study sought to evaluate the clinical utility of CDG for early coronary artery disease (CAD) detection in suspected patients with CAD presenting with nondiagnostic ECGs. A total of 421 suspected patients with CAD presenting with nondiagnostic ECG were enrolled. Standard 12-lead ECG and CDG were performed simultaneously, 1 day before invasive coronary angiography. Diagnostic accuracy of CDG for early CAD detection was assessed with reference to coronary angiography as the gold standard. Coronary angiography showed ≥1 coronary arteries stenosis of >50% in 347 patients. Of these 347 patients with CAD, 294 patients were positive in CDG. Of 74 non-CAD controls, 63 patients were negative in CDG. Diagnostic accuracy of CDG at presentation for CAD was 84.6%, sensitivity 84.7%, and specificity 83.7%. In patients presenting with nondiagnostic ECGs, an abnormal status can be detected early through noninvasive CDG. CDG is highly sensitive for the early diagnosis of CAD. Underlying causes of false-negative CDG findings included number of diseased coronary arteries and collateral circulation. Subtle myocardial damage that was not detectable on coronary angiography might be the major cause of false-positive findings. © 2016 Elsevier Inc.

Liu H.,South China University of Technology | Liu H.,National Engineering Research Center for Tissue Restoration and Reconstruction | Liu H.,Key Laboratory of Biomedical Engineering in Guangdong | Chen X.-F.,South China University of Technology | And 11 more authors.
Wuji Cailiao Xuebao/Journal of Inorganic Materials | Year: 2014

Bioactive glasses (BGs) exhibit potential applications for gene transfection because of their composition including Ca and P. Here, bioactive glass fibers (BGFs) with mesopores or hierarchical nanopores, were prepared by electrospinning process using BGs Sol-Gel precursor and its effect on mediating gene transfection was investigated. The results indicate that BGF acts as a gene vector by releasing Ca2+ and PO4 3-, and then reunioning them along with plasmid DNA in Dulbecco's Modified Eagle's Medium (DMEM). BGFs have a dose-dependent manner in transfection efficiency. When using 1 μg/mL plasmid, the transfection efficiency of BGF with concentration at 1000 μg/mL is higher than 50% of lipofectamine LTX_PLUS. The transfection mechanism of BGF is similar to that of calcium phosphate (CaP) system. Furthermore, BGF's sufficient ions releasing ensures stability and effectiveness to be applied in gene transfection, which makes BGF a promising candidate for gene delivery in replace of traditional gene carrier, the nano-calcium phosphate system.

Deng M.,South China University of Technology | Deng M.,Key Laboratory of Biomedical Engineering in Guangdong | Wang C.,South China University of Technology | Wang C.,Key Laboratory of Biomedical Engineering in Guangdong | And 2 more authors.
Pattern Recognition Letters | Year: 2016

Gait characteristics extracted from one single camera are limited and not comprehensive enough to develop a robust recognition system. This paper proposes a robust gait recognition method using multiple views fusion and deterministic learning. First, a multiple-views fusion strategy is introduced, in which gaits collected under different views are synthesized as a kind of synthesized silhouette images. Second, the synthesized silhouettes are characterized with four kinds of time-varying gait features, including three width features of the silhouette and one silhouette area feature. Third, gait variability underlying different individuals' time-varying gait features is effectively modeled by using deterministic learning algorithm. This kind of variability reflects the change of synthesized silhouettes while preserving temporal dynamics information of human walking. Gait patterns are represented as the gait variability underlying time-varying gait features and a rapid recognition scheme is presented in published gait databases. Experimental results show that encouraging recognition accuracy can be achieved. © 2016 Elsevier Ltd. All rights reserved.

Zeng W.,Longyan University | Wang C.,South China University of Technology | Wang C.,Key Laboratory of Biomedical Engineering in Guangdong
Neurocomputing | Year: 2016

Performance of gait recognition can be affected by many factors, especially by the variation of view angle which will significantly change the available visual features for matching. In this paper, we present a new method to eliminate the effect of view angle for efficient gait recognition via deterministic learning theory. The width of the binarized silhouette models the periodic deformation of human gait shape and is selected as the gait feature. It captures the spatio-temporal characteristics of each individual, represents the dynamics of gait motion, and sensitively reflects the variance between gait patterns across various views. The gait recognition approach consists of two phases: a training phase and a recognition phase. In the training phase, gait dynamics underlying different individuals' gaits observed from different view angles are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In order to address the problem of view change no matter the variation is small or significantly large, the training patterns from different views constitute a uniform training dataset containing all kinds of gait dynamics of each individual observed across various views. In the recognition phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of human gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gait pattern whose view pattern contained in the prior training dataset, a set of recognition errors are generated. The average L 1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. The test gait pattern similar to one of the training gait patterns can be recognized according to the smallest error principle. Finally, comprehensive experiments are carried out on the widely adopted multiview gait databases: CASIA-B and CMU MoBo to demonstrate the effectiveness of the proposed approach. © 2015 Elsevier B.V.

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