Automatic Neonatal Pain Estimation: An Acute Pain in Neonates Database

Pain assessment is a vital part of newborn treatment in Intensive Care Units. However, clinical pain assessment is highly subjective and does not support continual pain monitoring. Automated tools have been introduced to address this problem, but their performance is limited by inadequate training data and unsuitable pain annotations. In addition, current automated tools focus on pain detection rather than severity estimation, which is the unmet need in medical treatment. In this paper, we present: a) the Acute Pain in Neonates (APN-db) database, a public dataset to support research in this field and allow for benchmarking of new automated tools; b) a novel L1-point Neonatal Face and Limb Acute Pain Scale (NFLAPS), a visual behaviour-centric pain measurement tool, which is an adaptation of the Neonatal Infant Pain Scale (NIPS) and the Neonatal Facial Coding System (NFCS); and c) a system for neonatal pain assessment which encodes pain indicative-features using handcrafted algorithms and deep-learned features. Experiments show that our system performs well with an RMSE of 1.9 compared to human error of 1.65 on the same dataset, demonstrating its potential application to newborn health care.