scholarly journals Hierarchical support vector machine based heartbeat classification using higher order statistics and hermite basis function

2008 ◽  
Author(s):  
K.S. Park ◽  
B.H. Cho ◽  
D.H. Lee ◽  
S.H. Song ◽  
J.S. Lee ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Order Statistics ◽  
Basis Function ◽  
Higher Order ◽  
Support Vector ◽  
Higher Order Statistics ◽  
Heartbeat Classification

An Unsupervised Method based on Support Vector Machines and Higher-Order Statistics for Mechanical Faults Detection

2020 ◽  
Vol 18 (06) ◽  
pp. 1093-1101 ◽  
Author(s):  
Fernando Borges ◽  
Andrey Pinto ◽  
Diogo Ribeiro ◽  
Tassio Barbosa ◽  
Daniel Pereira ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Order Statistics ◽  
Higher Order ◽  
Support Vector ◽  
Higher Order Statistics ◽  
Unsupervised Method ◽  
Vector Machines ◽  
Mechanical Faults

AUTOMATED GLAUCOMA DETECTION USING CENTER SLICE OF HIGHER ORDER STATISTICS

2019 ◽  
Vol 19 (01) ◽  
pp. 1940011 ◽  
Author(s):  
RAHUL SHARMA ◽  
PRADIP SIRCAR ◽  
R. B. PACHORI ◽  
SULATHA V. BHANDARY ◽  
U. RAJENDRA ACHARYA
Keyword(s):  
Optic Nerve ◽  
Order Statistics ◽  
Classification Accuracy ◽  
Higher Order ◽  
Nerve Damage ◽  
Support Vector ◽  
Svm Classifier ◽  
Higher Order Statistics ◽  
Optic Nerve Damage ◽  
Glaucoma Detection

Glaucoma is one of the leading causes of blindness. The raised intraocular pressure is one of the important modifiable risk factor causing glaucomatous optic nerve damage. Glaucomatous optic nerve damage is seen as increase in the cupping of the optic disc and loss of neuroretinal rim. An automated detection system using nonlinear higher order statistics (HOS) based method is used to capture the detailed information present in the fundus image efficiently. The center slice of bispectrum and bicepstrum are applied on fundus images. Various features are extracted from the diagonal of these central slices. In order to reduce the number of features the locality sensitive discriminant analysis (LSDA) data reduction technique method is implemented. The ranked LSDA features are fed to support vector machine (SVM) classifier with various kernels for automated glaucoma detection. The simulation is performed on two databases. The proposed algorithm has yielded classification accuracy of 98.8% and 95% using entire private and public databases, respectively. The proposed technique achieved the highest classification accuracy, hence, confirm the diagnosis of ophthalmologists and can be employed in the community health care centers and hospitals.

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