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

IF 4.4 2区医学 Q2 ENGINEERING, BIOMEDICAL

IEEE Transactions on Biomedical Engineering Pub Date : 2024-09-02 DOI:10.1109/TBME.2024.3452708

Hyeon Seok Seok, Sang Su Kim, Do-Won Kim, Hangsik Shin

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引用次数: 0

Abstract

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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基于生物信号中的隐含反应，实现主观慢性疼痛的客观化。

目的：慢性疼痛需要早期干预和准确评估。目前基于主观问卷的方法存在局限性。本研究旨在开发一种基于多模态生物信号的慢性疼痛评估方法，并验证其可行性：方法：我们利用 59 名受试者（26 名慢性疼痛患者）的脑电图（EEG）、光脉搏图（PPG）、心电图（ECG）和面部温度（FT）数据建立了一个模型。从所有信号中共得出 112 个特征，其中 17 个特征显示慢性疼痛患者与正常对照组之间存在显著差异：结果：通过优化信号类型和特征组合，我们的疼痛分类模型显著增强了慢性疼痛评估能力（AUROC：0.802 至 0.864）。值得注意的特征包括 PPG 收缩长度（12.3%）、EEG α 波段功率（11.1%）和 delta 波段功率（9.4%）：这种多模态生物信号方法有望有效量化慢性疼痛。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Biomedical Engineering 工程技术-工程：生物医学

CiteScore

9.40

自引率

4.30%

发文量

880

审稿时长

2.5 months

期刊介绍： IEEE Transactions on Biomedical Engineering contains basic and applied papers dealing with biomedical engineering. Papers range from engineering development in methods and techniques with biomedical applications to experimental and clinical investigations with engineering contributions.