Objective: 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.
Methods: 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.
Results: 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%).
Conclusion: This multi-modal biosignal approach holds promise for effective chronic pain quantification.
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| http://dx.doi.org/10.1109/TBME.2024.3452708 | DOI Listing |
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