基于伪标签的疼痛领域分类器的调整
Tobias B Ricken1, Sascha Gruss2, Steffen Walter2
1Institute of Neural Information Processing, Ulm University, Ulm, Germany.
概括
这项研究通过使用伪标签从短期疼痛数据转移知识来增强长期疼痛评估. 这提高了电气和热疼痛刺激的分类准确性.
科学领域:
- 生物医学工程 生物医学工程
- 疼痛医学 医学 疼痛医学
- 机器学习 机器学习
背景情况:
- 疼痛感知是主观的,与短期疼痛相比,长期疼痛评估的数据很少.
- 现有的疼痛识别技术在短期疼痛方面表现良好,但在长期疼痛发作方面表现不佳.
- 准确的疼痛评估需要具有代表性的数据,需要长期疼痛分析的方法.
研究的目的:
- 将短期疼痛分类器适应长期疼痛领域.
- 为了应对长期疼痛评估中有限的标记数据的挑战.
- 为了提高疼痛评估系统对长期疼痛事件的准确性.
主要方法:
- 利用伪标签技术将知识从短期到长期疼痛领域转移.
- 分析了来自短期和长期疼痛记录的生理信号.
- 结合生理信号与电热疼痛刺激进行分析.
主要成果:
- 用伪标签的长期样本增加培训数据证明是有益的.
- 通过早期融合方法,电痛的分类性能提高了2.4% (至80.4%).
- 通过早期融合方法实现了热疼痛分类性能2.8%的改善 (至70.0%).
结论:
- 从短期到长期疼痛领域的知识转移对于准确的疼痛评估至关重要.
- 伪标签是一种有效的策略,可以使分类器适应长期疼痛领域.
- 提出的方法在分类长期疼痛发作方面取得了显著的改进.
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