Result: Machine assessment of neonatal facial expressions of acute pain
Title:
Machine assessment of neonatal facial expressions of acute pain
Authors:
Source:
Decision support systems. 43(4):1242-1254
Publisher Information:
Amsterdam: Elsevier Science, 2007.
Publication Year:
2007
Physical Description:
print, 67 ref
Original Material:
INIST-CNRS
Subject Terms:
Electronics, Electronique, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Théorie de la décision. Théorie de l'utilité, Decision theory. Utility theory, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Systèmes d'information. Bases de données, Information systems. Data bases, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Affichage, Display, Visualización, Age, Edad, Analyse composante principale, Principal component analysis, Análisis componente principal, Analyse discriminante, Discriminant analysis, Análisis discriminante, Analyse statistique, Statistical analysis, Análisis estadístico, Application médicale, Medical application, Aplicación medical, Apprentissage probabilités, Probability learning, Aprendizaje probabilidades, Classification, Clasificación, Faciès, Facies, Gestion réseau, Network management, Gestión red, Machine exemple support, Vector support machine, Máquina ejemplo soporte, Mimique, Facial expression, Mímica, Noyau(mathématiques), Kernels, Néonatal, Neonatal, Réseau neuronal, Neural network, Red neuronal, Système aide décision, Decision support system, Sistema ayuda decisíon, Linear discriminant analysis, Medical face classification, Neonate pain recognition, Neural network simultaneous optimization algorithm, Support vector machines
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Computer Information Systems, Missouri State University, 901 South National, Springfield, Missouri 65804, United States
Computer Vision Laboratory, College of Computing Sciences, New Jersey Institute of Technology, University Heights, Newark NJ 07102, United States
Computer Vision Laboratory, College of Computing Sciences, New Jersey Institute of Technology, University Heights, Newark NJ 07102, United States
ISSN:
0167-9236
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Operational research. Management
Operational research. Management
Accession Number:
edscal.19021999
Database:
PASCAL Archive
Further Information
We propose that a machine assessment system of neonatal expressions of pain be developed to assist clinicians in diagnosing pain. The facial expressions of 26 neonates (age 18-72h) were photographed experiencing the acute pain of a heel lance and three nonpain stressors. Four algorithms were evaluated on out-of-sample observations: PCA, LDA, SVMs and NNSOA. NNSOA provided the best classification rate of pain versus nonpain (90.20%), followed by SVM with linear kernel (82.35%). We believe these results indicate a high potential for developing a decision support system for diagnosing neonatal pain from images of neonatal facial displays.