Chatterjee Ayan, Riegler Michael A, Ganesh K, Halvorsen Pål
Oslo Metropolitan University (Oslomet), Oslo, Norway.
STIFTELSEN NILU, Kjeller, Norway.
Sci Rep. 2025 Feb 17;15(1):5755. doi: 10.1038/s41598-025-87510-w.
Heart Rate Variability (HRV) serves as a vital marker of stress levels, with lower HRV indicating higher stress. It measures the variation in the time between heartbeats and offers insights into health. Artificial intelligence (AI) research aims to use HRV data for accurate stress level classification, aiding early detection and well-being approaches. This study's objective is to create a semantic model of HRV features in a knowledge graph and develop an accurate, reliable, explainable, and ethical AI model for predictive HRV analysis. The SWELL-KW dataset, containing labeled HRV data for stress conditions, is examined. Various techniques like feature selection and dimensionality reduction are explored to improve classification accuracy while minimizing bias. Different machine learning (ML) algorithms, including traditional and ensemble methods, are employed for analyzing both imbalanced and balanced HRV datasets. To address imbalances, various data formats and oversampling techniques such as SMOTE and ADASYN are experimented with. Additionally, a Tree-Explainer, specifically SHAP, is used to interpret and explain the models' classifications. The combination of genetic algorithm-based feature selection and classification using a Random Forest Classifier yields effective results for both imbalanced and balanced datasets, especially in analyzing non-linear HRV features. These optimized features play a crucial role in developing a stress management system within a Semantic framework. Introducing domain ontology enhances data representation and knowledge acquisition. The consistency and reliability of the Ontology model are assessed using Hermit reasoners, with reasoning time as a performance measure. HRV serves as a significant indicator of stress, offering insights into its correlation with mental well-being. While HRV is non-invasive, its interpretation must integrate other stress assessments for a holistic understanding of an individual's stress response. Monitoring HRV can help evaluate stress management strategies and interventions, aiding individuals in maintaining well-being.
心率变异性（HRV）是压力水平的重要指标，HRV越低表明压力越大。它测量心跳之间时间的变化，并为健康状况提供洞察。人工智能（AI）研究旨在利用HRV数据进行准确的压力水平分类，有助于早期检测和健康管理方法。本研究的目标是在知识图谱中创建HRV特征的语义模型，并开发一个准确、可靠、可解释且符合伦理的AI模型用于预测性HRV分析。对包含压力状况下标记HRV数据的SWELL-KW数据集进行了检查。探索了各种技术，如特征选择和降维，以提高分类准确性同时最小化偏差。采用了不同的机器学习（ML）算法，包括传统方法和集成方法，来分析不平衡和平衡的HRV数据集。为了解决不平衡问题，试验了各种数据格式和过采样技术，如SMOTE和ADASYN。此外，使用了一种树状解释器，特别是SHAP，来解释和说明模型的分类。基于遗传算法的特征选择与随机森林分类器相结合，对不平衡和平衡数据集都产生了有效的结果，特别是在分析非线性HRV特征方面。这些优化后的特征在语义框架内开发压力管理系统中起着关键作用。引入领域本体增强了数据表示和知识获取。使用Hermit推理机评估本体模型的一致性和可靠性，将推理时间作为性能指标。HRV是压力的重要指标，为其与心理健康的相关性提供了洞察。虽然HRV是非侵入性的，但对其解释必须整合其他压力评估，以便全面了解个体的压力反应。监测HRV有助于评估压力管理策略和干预措施，帮助个体维持健康。
