SVM classification of neonatal facial images of pain

Authors:
Sheryl Brahnam;Chao-Fa Chuang;Frank Y. Shih;Melinda R. Slack
Affiliations:
Computer Information Systems, Missouri State University, Springfield, MO;New Jersey Institue of Technology, Newark, NJ;New Jersey Institue of Technology, Newark, NJ;Medical Director of Neonatology, St. John's Hospital, Springfield, MO
Venue:
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Year:
2005

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Abstract

This paper reports experiments that explore performance differences in two previous studies that investigated SVM classification of neonatal pain expressions using the Infant COPE database. This database contains 204 photographs of 26 neonates (age 18-36 hours) experiencing the pain of heel lancing and three nonpain stressors. In our first study, we reported experiments where representative expressions of all subjects were included in the training and testing sets, an experimental protocol suitable for intensive care situations. A second study used an experimental protocol more suitable for short-term stays: the SVMs were trained on one sample and then evaluated on an unknown sample. Whereas SVM with polynomial kernel of degree 3 obtained the best classification score (88.00%) using the first evaluation protocol, SVM with a linear kernel obtained the best classification score (82.35%) using the second protocol. However, experiments reported here indicate no significant difference in performance between linear and nonlinear kernels.