对各种疼痛体验的多模式见解:PhysioPain数据集
Boran Toktay1, İkbal Işık Orhan1, Elif Yıldırım2
1Department of Computer Engineering, Istanbul Kültür University, Istanbul, Türkiye.
Data in brief
|September 8, 2025
概括
物理疼痛数据集将EEG和EDA等生理信号与主观疼痛报告相结合. 这种综合性方法旨在改善各种疼痛类型的疼痛估计和分类.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 疼痛研究 疼痛研究
背景情况:
- 疼痛评估是复杂的,通常依赖于主观的报告.
- 客观的生理测量可以提供对疼痛经验的更深入的见解.
研究的目的:
- 介绍PhysioPain数据集,一个用于疼痛研究的多式联络资源.
- 通过结合生理和主观数据来实现复杂的疼痛表示.
- 促进疼痛估计和分类方面的进步.
主要方法:
- 收集了93名参与者的实时生理数据,包括脑电图 (EEG),皮肤温度,皮肤活动 (EDA),血液体积脉冲 (BVP) 和加速仪数据.
- 整合了客观的生理数据与麦吉尔大学问卷和定制调查的主观信息.
- 涵盖了各种各样的疼痛条件:没有疼痛,头痛,月经周期疼痛,背部/部/腰部疼痛.
主要成果:
- 该PhysioPain数据集提供了一个丰富的,多式联络的疼痛相关数据集合.
- 该数据集使疼痛强度和特征的全面分析成为可能.
- 它支持开发先进的疼痛评估工具.
结论:
- 物理疼痛数据集为疼痛强度提供了一个新的视角.
- 多模式数据集成增强了我们对复杂疼痛体验的理解.
- 这种资源对于疼痛估计,分类和观察有价值.
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