Relevance vector machine learning for neonate pain intensity assessment using digital imaging

IEEE Transactions on Biomedical Engineering

Volumn 57, Issue 6, 2010, Pages 1457-1466

  Gholami, Behnood ^a   Haddad, Wassim M ^a   Tannenbaum, Allen R ^b  

^a Georgia Institute of Technology   (United States)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Digital imaging; Facial expression recognition; Neonates; Pain assessment; Relevance vector machine (RVM); Support vector machine (SVM)

Indexed keywords

BAYESIAN; CLASSIFICATION TECHNIQUE; DEGREE OF CONFIDENCE; DIGITAL IMAGING; FACIAL EXPRESSION RECOGNITION; FACIAL EXPRESSIONS; FUNDAMENTAL LIMITATIONS; INTENSITY LEVELS; LEARNING TECHNIQUES; PAIN MANAGEMENT; PATTERN RECOGNITION TECHNIQUES; POSTERIOR PROBABILITY; RELEVANCE VECTOR MACHINE; SPARSER MODELS; SUBJECTIVE ASSESSMENTS; SUPPORT VECTOR MACHINE ALGORITHM;

BUBBLES (IN FLUIDS); FACE RECOGNITION; GESTURE RECOGNITION; HUMAN COMPUTER INTERACTION; LEARNING ALGORITHMS; PROBABILITY; SUPPORT VECTOR MACHINES;

VECTORS;

ALGORITHM; ARTICLE; BAYES THEOREM; DIGITAL IMAGING; DISEASE SEVERITY; FACIAL EXPRESSION; HUMAN; MACHINE LEARNING; NEWBORN; NEWBORN CARE; PAIN; PAIN ASSESSMENT; PATTERN RECOGNITION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFANT, NEWBORN; PAIN; PAIN MEASUREMENT; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; WHOLE BODY IMAGING;

EID: 77952579693     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2009.2039214     Document Type: Article

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