Sparse approximation of long-term biomedical signals for classification via dynamic PCA

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volumn , Issue , 2011, Pages 7167-7170

  Xie, Shengkun ^a   Jin, Feng ^a   Krishnan, Sridhar ^a  

^a RYERSON UNIVERSITY   (Canada)

Author keywords

Dynamic Principal Component Analysis; Feature Extraction; Signal Classification; Sparse Approximation

Indexed keywords

BIOMEDICAL SIGNAL; CLASSIFICATION ACCURACY; DYNAMIC PRINCIPAL COMPONENT ANALYSIS; EEG SIGNALS; ENERGY MEASURE; EPILEPTIC SEIZURE DETECTION; EVENT DETECTION; FEATURE INFORMATION; MOVING WINDOW; MULTIVARIATE STATISTICAL APPROACHES; NONSTATIONARY SIGNALS; NOVEL TECHNIQUES; PRINCIPAL COMPONENTS; SIGNAL CLASSIFICATION; SINGLE CHANNELS; SPARSE APPROXIMATIONS; SPARSE METHODS; SYNTHETIC DATA; SYNTHETIC DATABASE; UNIVARIATE;

BIOELECTRIC PHENOMENA; FEATURE EXTRACTION; MULTIVARIANT ANALYSIS; SIGNAL DETECTION;

PRINCIPAL COMPONENT ANALYSIS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; ELECTROENCEPHALOGRAPHY; EPILEPSY; FACTUAL DATABASE; HUMAN; METHODOLOGY; MULTIVARIATE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY; SIGNAL PROCESSING; STATISTICAL MODEL; TIME;

ALGORITHMS; DATABASES, FACTUAL; ELECTROENCEPHALOGRAPHY; EPILEPSY; HUMANS; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS; SIGNAL PROCESSING, COMPUTER-ASSISTED; TIME FACTORS;

EID: 84864582199     PISSN: 1557170X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2011.6091811     Document Type: Conference Paper

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