Quantifying and Characterizing Tonic Thermal Pain across Subjects from EEG Data using Random Forest Models

摘要

ObjectiveEffective pain assessment and management strategies are needed to better manage pain. In addition to self-report, an objective pain assessment system can provide a more complete picture of the neurophysiological basis for pain. In this study, a robust and accurate machine learning approach is developed to quantify tonic thermal pain across healthy subjects into a maximum of ten distinct classes.