Machine assessment of neonatal facial expressions of acute pain

Authors:
Sheryl Brahnam;Chao-Fa Chuang;Randall S. Sexton;Frank Y. Shih
Affiliations:
Computer Information Systems, Missouri State University, 901 South National, Springfield, Missouri 65804, USA;Computer Vision Laboratory, College of Computing Sciences, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA;Computer Information Systems, Missouri State University, 901 South National, Springfield, Missouri 65804, USA;Computer Vision Laboratory, College of Computing Sciences, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA
Venue:
Decision Support Systems
Year:
2007

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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 18-72h) 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.