Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients

Computers in Biology and Medicine

Volumn 38, Issue 3, 2008, Pages 401-410

  Übeyli, Elif Derya ^a  

^a TOBB UNIVERSITY OF ECONOMICS AND TECHNOLOGY   (Turkey)

Author keywords

Composite features; Electrocardiogram (ECG) signals; Levenberg Marquardt algorithm; Lyapunov exponents; Recurrent neural networks (RNNs); Wavelet coefficients

Indexed keywords

DATABASE SYSTEMS; ELECTROCARDIOGRAPHY; FEATURE EXTRACTION; LYAPUNOV FUNCTIONS; PATIENT MONITORING; SIGNAL PROCESSING;

COMPOSITE FEATURES; ELECTROCARDIOGRAM (ECG) SIGNALS; LEVENBERG-MARQUARDT ALGORITHM; LYAPUNOV EXPONENTS; WAVELET COEFFICIENTS;

RECURRENT NEURAL NETWORKS;

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; DIAGNOSTIC ACCURACY; ELECTROCARDIOGRAPHY; FOCAL EPILEPSY; HUMAN; PERFORMANCE; PRIORITY JOURNAL; SIGNAL DETECTION; WAVEFORM;

ALGORITHMS; ELECTROCARDIOGRAPHY; EPILEPSIES, PARTIAL; HUMANS; NEURAL NETWORKS (COMPUTER); ROC CURVE; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 39549123616     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2008.01.002     Document Type: Article

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