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在与生理信号工作时,对减轻阶级失衡的比较研究.

Rawan S Abdulsadig1, Esther Rodriguez-Villegas1

  • 1Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom.

Frontiers in digital health
|April 10, 2024
PubMed
概括

在医学事件检测中的类失衡,如呼吸暂停,可以通过再平衡算法来解决. 随机下标样改进了从PPG信号检测呼吸暂停的灵敏度,但可能会降低整体准确性.

科学领域:

  • 生物医学工程 生物医学工程
  • 医疗保健中的机器学习
  • 信号处理 信号处理

背景情况:

  • 阶级不平衡在检测罕见的医疗事件 (如呼吸暂停) 中具有重大挑战.
  • 光电解剖学 (PPG) 信号为非侵入性呼吸暂停检测提供了一个潜在的数据源.
  • 类再平衡技术对于缓解医学分类任务中的不平衡问题至关重要.

研究的目的:

  • 调查10种数据级类失衡缓解方法在检测呼吸暂停事件中的有效性.
  • 使用不平衡的PPG数据构建和评估一个随机森林 (RF) 模型.
  • 评估特征空间转换 (PCA,KernelPCA) 对类再平衡性能的影响.

主要方法:

  • 评估了十种类别不平衡缓解技术:RandUS,RandOS,CNNUS,ENNUS,TomekUS,SMOTE,BLSMOTE,ADASYN,SMOTETomek,以及SMOTEENN. 这三种类别不平衡的缓解技术包括:RandUS,RandOS,CNNUS,ENNUS,TomekUS,SMOTE,BLSMOTE,ADASYN,SMOTETomek和SMOTEENN.
  • 使用随机森林 (RF) 作为分类模型.
  • 探索主要组件分析 (PCA) 和KernelPCA用于特征空间转换.

主要成果:

  • 随机低采样 (RandUS) 显示了最显著的灵敏度改善,提高了高达11%的灵敏度.
关键词:
呼吸暂停 (apnea) 是一种阶级不平衡 阶级不平衡机器学习是机器学习.生理信号 生理信号在中突然意外死亡 (SUDEP)

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  • 虽然RandUS提高了灵敏度,但由于数据减少,它可能会降低整体准确性.
  • 数据增强技术,特别是对主题依赖性的增强技术,需要进一步的研究和开发.

    • 结论:

    • 随机US是一种可行的策略,可以通过PPG信号增强呼吸暂停检测灵敏度.
    • 在应用低采样方法时,必须仔细考虑灵敏度和准确度之间的权衡.
    • 需要先进的数据增强方法,以有效地处理不平衡的医疗数据集与固有的主体变化.