压力管理与HRV跟随AI,语义本体学,遗传算法和树解释器
Ayan Chatterjee1,2,3, Michael A Riegler4,5, K Ganesh6
1Oslo Metropolitan University (Oslomet), Oslo, Norway. ayanchat@oslomet.no.
Scientific reports
|February 17, 2025
概括
使用人工智能进行心率变化 (HRV) 分析,准确地分类压力水平. 这项研究开发了一种可解释的AI模型,用于预测HRV分析,增强压力管理系统.
科学领域:
- 使用人工智能 (AI) 和机器学习 (ML) 进行生物医学信号处理.
- 专注于医疗保健中的计算智能和知识表示.
- 集成数据科学与生理监测进行压力评估.
背景情况:
- 心率变化 (HRV) 是压力的关键生理指标,较低的HRV意味着更高的压力水平.
- 现有的人工智能研究旨在利用HRV数据进行精确的压力分类和早期福祉干预.
- SWELL-KW数据集提供了标记的HRV数据,这些数据对于训练和验证压力检测模型至关重要.
研究的目的:
- 在知识图中构建HRV特征的语义模型.
- 开发一个准确,可靠,可解释和道德的人工智能模型用于预测HRV分析.
- 通过优化HRV特征分析和语义框架来增强压力管理系统.
主要方法:
- 采用特征选择 (遗传算法) 和维度减小技术来优化HRV数据.
- 应用各种ML算法 (传统和整体,包括随机森林分类器) 对不平衡和平衡的数据集.
- 使用过量采样技术 (SMOTE,ADASYN) 和SHAP用于模型解释性和偏差缓解.
主要成果:
- 基于遗传算法的特征选择和随机森林分类器的组合显示了压力分类的高准确性.
- 非线性HRV特征在与优化的特征集分析时特别有效.
- 具有域本体学的语义框架改善了数据表示和知识获取,并得到了Hermit reasoners的验证.
结论:
- 开发的AI模型提供了基于HRV的准确,可解释和道德的压力水平分类.
- 优化的HRV功能对于在语义上有效的压力管理系统至关重要.
- 当与其他评估和语义见解集成时,HRV监测提供了一种全面的方法来理解和管理压力.
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