Multi-level Pain Quantification using a Smartphone and Electrodermal Activity

Appropriate prescription of pain medication is challenging because pain is difficult to quantify due to the subjectiveness of pain assessment. Currently, clinicians must entirely rely on pain scales based on patients' assessments. This has been alleged to be one of the causes of drug overdose and addiction, and a contributor to the opioid crisis. Therefore, there is an urgent unmet need for objective pain assessment. Furthermore, as pain can occur anytime and anywhere, ambulatory pain monitoring would be welcomed in practice. In our previous study, we developed electrodermal activity (EDA)-derived indices and implemented them in a smartphone application that can communicate via Bluetooth to an EDA wearable device. While we previously showed high accuracy for high-level pain detection, multi-level pain detection has not been demonstrated. In this paper, we tested our smartphone application with a multi-level pain-induced dataset. The dataset was collected from fifteen subjects who underwent four levels of pain-inducing electrical pulse (EP) stimuli. We then performed statistical analyses and machine-learning techniques to classify multiple pain levels. Significant differences were observed in our EDA-derived indices among no-pain, low-pain, and high-pain segments. A random forest classifier showed 62.6% for the balanced accuracy, and a random forest regressor exhibited 0.441 for the coefficient of determination. Clinical Relevance - This is one of the first studies to present a smartphone application for detecting multiple levels of pain in real time using an EDA wearable device. This work shows the feasibility of ambulatory pain monitoring which can potentially be useful for chronic pain management.