Automatic Infants' Pain Assessment by Dynamic Facial Representation: Effects of Profile View, Gestational Age, Gender, and Race.

Ruicong Zhi,Yu Sun,Dmitry Goldgof,Ghada Zamzmi Dmitry Zamzmi,Terri Ashmeade

doi:10.3390/jcm7070173

Abstract

Infants’ early exposure to painful procedures can have negative short and long-term effects on cognitive, neurological, and brain development. However, infants cannot express their subjective pain experience, as they do not communicate in any language. Facial expression is the most specific pain indicator, which has been effectively employed for automatic pain recognition. In this paper, dynamic pain facial expression representation and fusion scheme for automatic pain assessment in infants is proposed by combining temporal appearance facial features and temporal geometric facial features. We investigate the effects of various factors that influence pain reactivity in infants, such as individual variables of gestational age, gender, and race. Different automatic infant pain assessment models are constructed, depending on influence factors as well as facial profile view, which affect the model ability of pain recognition. It can be concluded that the profile-based infant pain assessment is feasible, as its performance is almost as good as that of the whole face. Moreover, gestational age is the most influencing factor for pain assessment, and it is necessary to construct specific models depending on it. This is mainly because of a lack of behavioral communication ability in infants with low gestational age, due to limited neurological development. To our best knowledge, this is the first study investigating infants’ pain recognition, highlighting profile facial views and various individual variables.

Highlights

Healthcare for infants in a Neonatal Intensive Care Unit (NICU) is critical for survival; the hospitalization stage affects their neurodevelopment and future growth
The temporal facial texture feature of gradient representation is denoted by DAGradient, and Local Binary Pattern (LBP)-TOP representation is denoted by DALBP-TOP
The facial features are low-dimensional data extracted by Supervised Locality Preserving Projections (SLPP) and employed to train Support Vector Machine (SVM) classifiers individually

Summary

Introduction

Healthcare for infants in a Neonatal Intensive Care Unit (NICU) is critical for survival; the hospitalization stage affects their neurodevelopment and future growth. Invasive medical interventions are often required in clinical treatment, and as a result, infants suffer from repeated procedural pain as part of their general care in the NICU. Accurate pain assessment is comprehensive and multidimensional, and the commonly used pain measurements include contextual, behavioral, and physiological tools [2]. Multidimensional indicator-based scales are commonly used for infants’ pain assessment, such as Neonatal Infant Pain Scale (NIPS) [9], Face, Legs, Activity, Crying, and Consolability (FLACC) [10], Neonatal Facial Coding System (NFCS) [11] for acute pain assessment, Neonatal Pain, Agitation, and Sedation Scale (N-PASS) [12], Neonatal Pain and Discomfort Scale (EDIN) [13], and Crying, Requires O2, Increased VS, Expression, and Sleepless (CRIES) [14] for chronic pain assessment

Methods

Results

Conclusion