Title
Machine assessment of neonatal facial expressions of acute pain
Abstract
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 18–72 h) 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.
Department(s)
Information Technology and Cybersecurity
Document Type
Article
DOI
https://doi.org/10.1016/j.dss.2006.02.004
Keywords
neonate pain recognition, medical face classification, support vector machines, linear discriminant analysis, principal component analysis, neural network simultaneous optimization algorithm
Publication Date
2007
Recommended Citation
Brahnam, Sheryl, Chao-Fa Chuang, Randall S. Sexton, and Frank Y. Shih. "Machine assessment of neonatal facial expressions of acute pain." Decision Support Systems 43, no. 4 (2007): 1242-1254.
Journal Title
Decision Support Systems
