Machine assessment of neonatal facial expressions of acute pain
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 hours) 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.
Keywords: Neonatal Pain Recognition, Medical Face Classification, Support Vector Machines, Linear Discriminant Analysis, Principal Component Analysis, Neural Network Simultaneous Optimization Algorithm