Real-time epileptic seizure prediction using AR models and support vector machines

IEEE Transactions on Biomedical Engineering

Volumn 57, Issue 5, 2010, Pages 1124-1132

  Chisci, Luigi ^a   Mavino, Antonio ^a   Perferi, Guido ^a   Sciandrone, Marco ^a   Anile, Carmelo ^b   Colicchio, Gabriella ^b   Fuggetta, Filomena ^b  

^a UNIVERSITY OF FLORENCE   (Italy)

^b CATHOLIC UNIVERSITY   (Italy)

Author keywords

Autoregressive (AR) models; EEG signals; Epileptic seizure prediction; Kalman filtering; Support vector machines (SVMs)

Indexed keywords

AR MODELS; AUTO REGRESSIVE MODELS; AUTOREGRESSIVE MODELING; BINARY CLASSIFICATION; COMPUTATIONAL REQUIREMENTS; DATA SETS; EEG SIGNALS; EPILEPTIC PATIENTS; EPILEPTIC SEIZURE PREDICTION; EPILEPTIC SEIZURES; FALSE ALARM RATE; KALMAN-FILTERING; LEAST SQUARE; ON-LINE ANALYSIS; PARAMETER ESTIMATORS; REAL-TIME IMPLEMENTATIONS; SVM CLASSIFIERS;

ELECTROENCEPHALOGRAPHY; FEATURE EXTRACTION; FORECASTING; GEARS; KALMAN FILTERS; MULTILAYER NEURAL NETWORKS; REAL TIME CONTROL; TIME SERIES; VECTORS;

SUPPORT VECTOR MACHINES;

ARTICLE; AUTOREGRESSIVE MODEL; ELECTROENCEPHALOGRAPHY; FILTER; KALMAN FILTER; MATHEMATICAL PARAMETERS; ONLINE ANALYSIS; PREDICTIVE VALIDITY; SEIZURE; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SYSTEMS; DATA INTERPRETATION, STATISTICAL; DIAGNOSIS, COMPUTER-ASSISTED; ELECTROENCEPHALOGRAPHY; EPILEPSY; HUMANS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 77951267316     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2009.2038990     Document Type: Article

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