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)

Management and Information Technology

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

Journal Title

Decision Support Systems

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