Towards Continuous Acute Pain Detection using Deep Learning and Electrodermal Activity

Abstract

Measuring pain objectively, namely, based on physiological signals instead of self-reported measures, would be highly valuable for better treating people with chronic pain. The subjectivity of the gold standard to quantify pain, which is based upon subjects' self-reported assessment using numerical or visual scales, makes pain management extremely complicated and, in many cases, has led to abuse of pain medication. Electrodermal activity (EDA) is a highly sensitive measure of sympathetic activity and has been increasingly used to objectively assess pain. In this study, we evaluated convolutional neural networks (CNN) and long short-term memory (LSTM) architectures for the task of detecting pain continuously. Additionally, we tested the use of the time-frequency spectrum of the phasic component of the electrodermal activity, as feature for this task. We used a merged database composed of thirty-six healthy subjects that underwent heat pain stimuli by means of a thermal grill. The LSTM models obtained better performance than the CNN ones by more of 3% in the F1-Score. Moreover, the best performance was achieved by a stacked bi- and uni-directional LSTM architecture, with 75.3% F1-Score, being able to accurately detect the onset and end of the pain response on EDA. Continuous objective pain detection using deep learning can contribute to continuous monitoring pain sensation and to reduce the consequences of subjectiveness of current pain assessment methods.