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自动电皮活动和面部表情分析用于X-ITE疼痛数据库上的持续疼痛强度监测.

Ehsan Othman1, Philipp Werner1, Frerk Saxen1

  • 1Department of Neuro-Information Technology, Institute for Information Technology and Communications, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany.

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概括

这项研究引入了一种自动化系统,用于使用皮电活动 (EDA) 和面部表情监测患者的疼痛. 结合这些方法,特别是EDA,为改善医疗保健质量提供了对患者疼痛经历的可靠见解.

关键词:
连续疼痛强度识别识别连续疼痛强度识别电皮活动电皮活动.面部表情 面部表情融合 融合 融合 融合 融合 融合 融合 融合 融合 融合长期短期内存 网络内存 网络内存随机的森林随机的森林抽样权重的使用方法

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科学领域:

  • 生物医学工程 生物医学工程
  • 医疗保健技术 技术 医疗保健 技术
  • 疼痛管理 疼痛管理

背景情况:

  • 持续的患者疼痛监测对于医疗保健质量至关重要.
  • 电皮活动 (EDA) 和面部表情是疼痛强度的关键指标.
  • 之前的研究已经分别探索了这些模式.

研究的目的:

  • 开发和评估用于持续监测患者疼痛强度的自动化系统.
  • 为了比较电皮活动 (EDA) 和面部表情分析的有效性.
  • 调查EDA和面部表情数据晚期融合的好处.

主要方法:

  • 使用电皮活动 (EDA) 传感器和面部表情分析.
  • 应用机器学习模型:随机森林 (RF),长短期记忆网络 (LSTM) 和具有样本权重的LSTM (LSTM-SW).
  • 采用了晚期融合技术,将两种模式的数据结合起来.

主要成果:

  • 在平衡的数据集中,EDA和面部表情的晚期融合显示出有效性 (微F1得分~61%,ICC~0.35).
  • 在不平衡的数据集中,LSTM和LSTM-SW模型在EDA回归方面表现出卓越的性能.
  • 综合和EDA-only方法的表现都超过了基线和随机猜测方法.

结论:

  • 整合EDA和面部表情模式为患者的疼痛提供了宝贵的见解.
  • 基于EDA的回归模型,特别是LSTM变体,对于疼痛监测是有效的,特别是在不平衡的场景中.
  • 开发的系统可以提高医疗中心评估和响应患者疼痛的能力.