Toward Objectification of Subjective Chronic Pain Based on Implicit Response in Biosignals.

Abstract

Chronic pain necessitates early intervention and accurate evaluation. Current subjective questionnaire -based methods have limitations. This study aims to develop a chronic pain assessment method based on multi-modal biosignal and to validate its feasibility. We present a model utilizing electroencephalogram (EEG), photoplethysmogram (PPG), electrocardiogram (ECG), and facial temperature (FT) data from 59 subjects (26 chronic pain patients). A total of 112 features were derived from all signals, and 17 of them showed a significant difference between the chronic pains and the normal control. By optimizing signal types and feature combinations, our pain classification model significantly enhanced chronic pain assessment (AUROC: 0.802 to 0.864). Notable features included PPG systolic length (12.3%), EEG alpha band power (11.1%), and delta band power (9.4%). This multi-modal biosignal approach holds promise for effective chronic pain quantification.