A NEW PARAMETRIC FEATURE DESCRIPTOR FOR THE CLASSIFICATION OF EPILEPTIC AND CONTROL EEG RECORDS IN PEDIATRIC POPULATION

2012 ◽  
Vol 22 (02) ◽  
pp. 1250001 ◽  
Author(s):  
MERCEDES CABRERIZO ◽  
MELVIN AYALA ◽  
MOHAMMED GORYAWALA ◽  
PRASANNA JAYAKAR ◽  
MALEK ADJOUADI
Keyword(s):  
Pediatric Population ◽  
Support Vector ◽  
Feature Descriptor ◽  
Scalp Eeg ◽  
Vector Machines ◽  
Epileptic Patients ◽  
Sensitivity Specificity ◽  
And Control

This study evaluates the sensitivity, specificity and accuracy in associating scalp EEG to either control or epileptic patients by means of artificial neural networks (ANNs) and support vector machines (SVMs). A confluence of frequency and temporal parameters are extracted from the EEG to serve as input features to well-configured ANN and SVM networks. Through these classification results, we thus can infer the occurrence of high-risk (epileptic) as well as low risk (control) patients for potential follow up procedures.

scholarly journals Classification of hyperspectral endocrine tissue images using support vector machines

2020 ◽  
Vol 16 (5) ◽  
pp. 1-10
Author(s):  
Marianne Maktabi ◽  
Hannes Köhler ◽  
Magarita Ivanova ◽  
Thomas Neumuth ◽  
Nada Rayes ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Endocrine Tissue ◽  
Vector Machines

scholarly journals Support vector machines for classification of input vectors with different metrics

2011 ◽  
Vol 61 (9) ◽  
pp. 2874-2878 ◽  
Author(s):  
L. Gonzalez-Abril ◽  
F. Velasco ◽  
J.A. Ortega ◽  
L. Franco
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines

A Hybrid Methodology For On-Line Process Monitoring

2004 ◽  
Vol 2 (1) ◽  
Author(s):  
Rakesh Kumar ◽  
Avinash M. Jade ◽  
Valadi K. Jayaraman ◽  
Bhaskar D. Kulkarni
Keyword(s):  
Process Monitoring ◽  
Current Practice ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Nonlinear Dimensionality Reduction ◽  
Hybrid Strategy ◽  
Vector Machines ◽  
On Line ◽  
Line Process

A hybrid strategy of using (i) locally linear embedding for nonlinear dimensionality reduction of high dimensional data and (ii) support vector machines for classification of the resultant features is proposed as a robust methodology for process monitoring. Illustrative examples substantiate the methodology vis-à-vis current practice.

scholarly journals Detection and Classification of Organophosphate Nerve Agent Simulants Using Support Vector Machines with Multiarray Sensors

2004 ◽  
Vol 44 (2) ◽  
pp. 499-507 ◽  
Author(s):  
Omowunmi Sadik ◽  
Walker H. Land, ◽  
Adam K. Wanekaya ◽  
Michiko Uematsu ◽  
Mark J. Embrechts ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Nerve Agent ◽  
Support Vector ◽  
Vector Machines

Accurate Classification of Burn Injuries Using Support Vector Machines and the Wavelet Shannon Entropy of the THz-TDS Waveforms

2021 ◽  
Author(s):  
Mahmoud E. Khani ◽  
Zachery B. Harris ◽  
Omar B. Osman ◽  
Juin-Wan Zhou ◽  
Adam J. Singer ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Shannon Entropy ◽  
Burn Injuries ◽  
Support Vector ◽  
Vector Machines
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