Machine assessment of neonatal facial expressions of acute pain

Sheryl Brahnam, Chao Fa Chuang, Randall S. Sexton, Frank Y. Shih

Research output: Contribution to journalArticlepeer-review

65 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1242-1254
Number of pages13
JournalDecision Support Systems
Volume43
Issue number4
DOIs
StatePublished - Aug 2007

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

Keywords

  • Linear discriminant analysis
  • Medical face classification
  • Neonate pain recognition
  • Neural network simultaneous optimization algorithm
  • Principal component analysis
  • Support vector machines

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