Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Artificial Intelligence in Medicine

Volumn 53, Issue 2, 2011, Pages 83-95

  Lima, Clodoaldo A M ^a   Coelho, André L V ^b  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b UNIVERSITY OF FORTALEZA   (Brazil)

Author keywords

EEG signal classification; Epilepsy; Feature extraction; Kernel machines

Indexed keywords

ACCURACY RATE; COMPARATIVE STUDIES; CROSS VALIDATION; EEG RECORDING; EEG SIGNAL CLASSIFICATION; EEG SIGNALS; ELECTROENCEPHALOGRAM SIGNALS; EPILEPSY; EPILEPTIC PATIENTS; EXPONENTIAL RADIAL BASIS; FEATURE EXTRACTOR; GAUSSIANS; KERNEL FUNCTION; KERNEL MACHINE; KERNEL PARAMETER; KERNEL-BASED LEARNING; LAGRANGIAN; LEAST SQUARE; LYAPUNOV EXPONENT; OPTIMALITY; PARAMETER VALUES; PERFORMANCE LEVEL; PREDICTIVE ACCURACY; RELEVANCE VECTOR MACHINE; SENSITIVITY PROFILES; SIGNAL REPRESENTATIONS; SYSTEMATIC ASSESSMENT; WAVELET BASIS; WAVELET BASIS FUNCTIONS;

DIFFERENTIAL EQUATIONS; DISCRETE WAVELET TRANSFORMS; FEATURE EXTRACTION; LEAST SQUARES APPROXIMATIONS; LYAPUNOV FUNCTIONS; LYAPUNOV METHODS; RADIAL BASIS FUNCTION NETWORKS; SENSITIVITY ANALYSIS; STANDARDS; SUPPORT VECTOR MACHINES;

ELECTROENCEPHALOGRAPHY;

ARTICLE; CALIBRATION; CLINICAL ARTICLE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DISEASE CLASSIFICATION; ELECTROENCEPHALOGRAM; EPILEPSY; HUMAN; KERNEL METHOD; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; VALIDATION PROCESS;

ARTIFICIAL INTELLIGENCE; ELECTROENCEPHALOGRAPHY; EPILEPSY; HUMANS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINES;

EID: 80052511414     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2011.07.003     Document Type: Article

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